VISAPP 2008 Abstracts


Area 1 - Image Formation and Processing

Full Papers
Paper Nr: 54
Title:

MULTIPLE VIEW GEOMETRY FOR MIXED DIMENSIONAL CAMERAS

Authors:

Kazuki Kozuka and Jun Sato

Abstract: In this paper, we analyze the multiple view geometry under the case where various dimensional imaging sensors are used together. Although the multiple view geometry has been studied extensively and extended for more general situations, all the existing multiple view geometries assume that the scene is observed by the same dimensional imaging sensors, such as 2D cameras. In this paper, we show that there exist multilinear constraints on image coordinates, even if the dimensions of camera images are different each other. The new multilinear constraints can be used for describing the geometric relationships between 1D line sensors, 2D cameras, 3D range sensors etc., and for calibrating mixed sensor systems.
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Paper Nr: 86
Title:

ACCURACY IMPROVEMENTS AND ARTIFACTS REMOVAL IN EDGE BASED IMAGE INTERPOLATION

Authors:

Nicola Asuni and Andrea Giachetti

Abstract: In this paper we analyse the problem of general purpose image upscaling that preserves edge features and natural appearance and we present the results of subjective and objective evaluation of images interpolated using different algorithms. In particular, we consider the well-known NEDI (New Edge Directed Interpolation, Li and Orchard, 2001) method, showing that by modifying it in order to reduce numerical instability and making the region used to estimate the low resolution covariance adaptive, it is possible to obtain relevant improvements in the interpolation quality. The implementation of the new algorithm (iNEDI, improved New Edge Directed Interpolation), even if computationally heavy (as the Li and Orchard’s method), obtained, in both subjective and objective tests, quality scores that are notably higher than those obtained with NEDI and other methods presented in the literature.
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Paper Nr: 118
Title:

IMAGE INPAINTING CONSIDERING BRIGHTNESS CHANGE AND SPATIAL LOCALITY OF TEXTURES

Authors:

Norihiko Kawai, Tomokazu Sato and Naokazu Yokoya

Abstract: Image inpainting is a tequnique for removing undesired visual objects in images and filling the missing regions with plausible textures. Conventionally, the missing parts of an image are completed by optimizing the objective function, which is defined based on pattern similarity between the missing region and the rest of the image (data region). However, unnatural textures are easily generated due to two factors: (1) available samples in the data region are quite limited, and (2) pattern similarity is one of the required conditions but is not sufficient for reproducing natural textures. In this paper, in order to improve the image quality of completed texture, the objective function is extended by allowing brightness changes of sample textures (for (1)) and introducing spatial locality as an additional constraint (for (2)). The effectiveness of these extensions is successfully demonstrated by applying the proposed method to one hundred images and comparing the results with those obtained by the conventional methods.
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Paper Nr: 132
Title:

CONSTRAIN PROPAGATION FOR GHOST REMOVAL IN HIGH DYNAMIC RANGE IMAGES

Authors:

Matteo Pedone and Janne Heikkilä

Abstract: Creating high dynamic range images of non-static scenes is a challenging task. Carefully preventing strong camera shakes during shooting and performing image-registration before combining the exposures cannot ensure that the resulting HDR image is consistent. This is eventually due to the presence of moving objects in the scene that causes the so called ghosting artifacts. Currently there is no robust solution that produces satisfactory results in any circumstance. Our method consists of two main steps. First, the probability of belonging to the static part of the scene is estimated for each pixel of the N exposures, yielding N weight images. In the second phase, we segment the areas of the weight-images with lower and higher probability values, and smoothly propagate their influence until a significant change in luminosity is detected or a pixel with a corresponding high probability of belonging to the background is approached. This represents an attempt to spread the influence of lower weights to the surrounding pixels of the same object. Results produced with our technique show a significant reduction or total removal of ghosting artifacts.
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Paper Nr: 145
Title:

ACCELERATED SKELETONIZATION ALGORITHM FOR TUBULAR STRUCTURES IN LARGE DATASETS BY RANDOMIZED EROSION

Authors:

Gerald A. Zwettler, Franz Pfeifer, Roland Swoboda and Werner Backfrieder

Abstract: Skeletonization is an important procedure in morphological analysis of three-dimensional objects. A simplified object geometry allows easy semantic interpretation at the cost of high computational effort. This paper introduces a fast morphological thinning approach for skeletonization of tubular structures and objects of arbitrary shape. With minimized constraints for erosions at the surface, hit-ratio is increased allowing high performance thinning with large datasets. Time consuming neighbourhood checking is solved by use of fast indexing lookup tables. The novel algorithm homogenously erodes the object’s surface, resulting in an accurate extraction of the centerline, even when the medial axis is placed between the actual voxel-grid. The thinning algorithm is applied for vessel tree analysis in the field of computer-based medical diagnostics and thus has to meet high robustness and performance requirements.
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Paper Nr: 208
Title:

SELF-CALIBRATION OF CENTRAL CAMERAS BY MINIMIZING ANGULAR ERROR

Authors:

Juho Kannala, Sami S. Brandt and Janne Heikkilä

Abstract: This paper proposes a generic self-calibration method for central cameras. The method requires two-view point correspondences and estimates both the internal and external camera parameters by minimizing angular error. In the minimization, we use a generic camera model which is suitable for central cameras with different kinds of radial distortion models. The proposed method can be hence applied to a large range of cameras from narrow-angle to fish-eye lenses and catadioptric cameras. Here the camera parameters are estimated by minimizing the angular error which does not depend on the 3D coordinates of the point correspondences. However, the error still has several local minima and in order to avoid these we propose a multi-step optimization approach. This strategy also has the advantage that it can be used together with RANSAC to provide robustness for false matches. We demonstrate our method in experiments with synthetic and real data.
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Paper Nr: 275
Title:

A STUDY ON ILLUMINATION NORMALIZATION FOR 2D FACE VERIFICATION

Authors:

Qian Tao and Raymond Veldhuis

Abstract: Illumination normalization is very important for 2D face verification. This study examines the state-of-art illumination normalization methods, and proposes two solutions, namely horizontal Gaussian derivative filters and local binary patterns. Experiments show that our methods significantly improve the generalization capability, while maintaining good discrimination capability of a face verification system. The proposed illumination normalization methods have low requirements on image acquisition, and low computation complexities, and are very suitable for low-end 2D face verification systems.
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Paper Nr: 317
Title:

EVOLVING ROI CODING IN H.264 SVC

Authors:

Shamikha Shah and Eran Edirisinghe

Abstract: Region-of-Interest (ROI) based coding is an integral feature of most image/video coding techniques/standards and has im-portant applications in content based video coding, storage and transmission. However, in the latest scalable extension of H.264 AVC video coding standard, i.e. H.264 SVC, motion estimation across the slice group boundaries does not preserve the coding quality and compression rate of the ROI. In this paper novel enhancements to the ROI based coding for H.264 SVC have been proposed to constrain the inter frame prediction across slice group boundaries. We show that the proposed algorithms do not negatively affect the rate-distortion performance of the coded video, but provide useful additional functionality that enables the extended use of the standard in many new application domains. Further, we pro-pose a method for supporting the coding of moving ROI in the scalable video coding domain, by adaptively changing the shape, size and position of the slice groups. We show that this additional functionality is particularly useful in video surveil-lance applications to effectively compress and transmit the ROI and reduce the storage and transmission requirements without any quality degradation of the ROI.
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Paper Nr: 342
Title:

LIMITED ANGLE IMAGE RECONSTRUCTION USING FOUR HIGH RESOLUTION PROJECTION AXES AT CO-PRIME RATIO VIEW ANGLES

Authors:

Anastasios Kesidis

Abstract: This paper proposes a sequential image reconstruction algorithm for the exact reconstruction of an image from a limited number of projection angles. Specifically, four projection axes oriented at coprime ratio view angles are used. The set of proper values for the view angles as well as the overall number of samples on the projection axis are explicitly defined and are related only to the dimensions of the image. The slopes of the four projection axes are calculated according to the chosen view angle and are symmetrically oriented with respect to the horizontal and the vertical axis. The reconstruction is a non-iterative, one pass process based on a decomposition sequence which defines the order in which the image pixels are restored. Several simulation results are provided that demonstrate the feasibility of the proposed method.
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Paper Nr: 371
Title:

EDGE-PRESERVING SMOOTHING OF NATURAL IMAGES BASED ON GEODESIC TIME FUNCTIONS

Authors:

Jacopo Grazzini and Pierre Soille

Abstract: In this paper, we address the problem of edge-preserving smoothing of natural images. We introduce a novel adaptive approach derived from mathematical morphology as a preprocessing stage in feature extraction and/or image segmentation. Like other filtering methods, it assumes that the local neighbourhood of a pixel contains the essential information required for the estimation of local image properties. It performs a weighted averaging by combining both spatial and tonal information in a single similarity measure based on the local calculation of geodesic time functions from the estimated pixel. By designing relevant geodesic masks, it can deal with specific situation and type of images. We describe in the following two possible strategies and we show their capabilities at smoothing heterogeneous areas while preserving relevant structures in natural - greyscale or multispectral - images displaying different features.
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Short Papers
Paper Nr: 19
Title:

BACKGROUND SEGMENTATION IN MICROSCOPY IMAGES

Authors:

James Charles, L. I. Kuncheva and B. Wells

Abstract: In many applications it is necessary to segment the foreground of an image from the background. However images from microscope slides illuminated using transmitted light have uneven background light levels. The non-uniform illumination makes segmentation difficult. We propose to fit a set of parabolas in order to segment the image into background and foreground. Parabolas are fitted separately on horizontal and vertical stripes of the grey level intensity image. A pixel is labelled as background or foreground based on the two corresponding parabolas. The proposed method outperforms the following four standard segmentation techniques, (1) thresholding determined manually or by fitting a mixture of Gaussians, (2) clustering in the RGB space, (3) fitting a two-argument quadratic function on the whole image and (4) using the morphological closure method.
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Paper Nr: 143
Title:

ANISOTROPIC DIFFUSION BY QUADRATIC REGULARIZATION

Authors:

Marcus Hund and Bärbel Mertsching

Abstract: Based on a regularization formulation of the problem, we present a novel approach to anisotropic diffusion that brings up a clear and easy-to-implement theory containing a problem formulation with existence and uniqueness of the solution. Unlike many iterative applications, we present a clear condition for the step size ensuring the convergence of the algorithm. The capability of our approach is demonstrated on a variety of well known test images.
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Paper Nr: 170
Title:

HISTORICAL DOCUMENT IMAGE BINARIZATION

Authors:

Carlos B. Mello, Adriano Oliveira and Angel Sanchez

Abstract: Preservation and publishing historical documents are important issues which have gained more and more interest over the years. Digital media has been used to storage digital versions of the documents as image files. However, this digital image needs huge storage space as usually the documents are digitized in high resolutions and in true colour for preservation purposes. In order to make easier the access to the images they can be converted into bi-level images. We present in this work a new method composed by two algorithms for binarization of historical document images based on Tsallis entropy. The new method was compared to several other well-known threshold algorithms and it achieved the best qualitative and quantitative results when compared to the gold standard images of the documents, measuring precision, recall, accuracy, specificity, peak signal-to-noise ratio and mean square error.
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Paper Nr: 171
Title:

A NEW RELIABILITYMEASURE FOR ESSENTIAL MATRICES SUITABLE IN MULTIPLE VIEWCALIBRATION

Authors:

Jaume Verges-Llahi, Daniel Moldovan and Toshikazu Wada

Abstract: This paper presents a new technique to recover structure and motion from a large number of images acquired by an intrinsically calibrated perspective camera. We describe a method for computing reliable camera motion parameters that combines a camera–dependency graph, which describes the set of camera locations and the feasibility of pairwise motion calculations, and an algorithm for computing the weights on the edges of this graph. A new criterion for evaluating the reliability of the essential matrices thus produced with respect to the epipolar constraint is here introduced. It is composed of two main elements, namely, the uncertainty of the renormalization process by which the essential matrix is derived and the error between the estimated matrix and its decomposition into the motion parameters of translation and rotation. Experimental results show that there exists a clear correlation between the proposed reliability measure and the error in the estimation of such motion parameters. The performance of the proposed method is demonstrated on a sequence of short base-line images where it is made clear that the strategy based on the shortest paths in terms of unreliability provides remarkably superior results to those obtained from the paths of consecutive camera locations.
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Paper Nr: 224
Title:

AN IMPROVED APPROACH FOR THINNING BY PRESERVING LOCAL COUPLING POINTS

Authors:

Abstract: Image thinning, while preserving geometrical properties of the image, remains one of the most challenging areas in Image Processing. In this paper, we introduce an image thinning approach that takes as input gray scale images and produces binary thinned images as output. Our approach uses bi-directional iterative thinning process to get single-pixel-thinned image (primary skeleton), while preserving the geometric properties of the image. The gray scale image is binarized using dyamic threshold, and further processed to remove noise. Black pixels are classified as contour pixels (pixels exposed to the background) and body pixels (black pixels other than the contour pixels). Thinning process involves scanning contour pixels from two opposite directions simultaneously, while preserving Local Coupling Points (LCP) and removing the rest of pixels.

Paper Nr: 236
Title:

FILLING-IN GAPS IN TEXTURED IMAGES USING BIT-PLANE STATISTICS

Authors:

Edoardo Ardizzone, Haris Dindo and Giuseppe Mazzola

Abstract: In this paper we propose a novel approach for the texture analysis-synthesis problem, with the purpose to restore missing zones in greyscale images. Bit-plane decomposition is used, and a dictionary is build with bit-blocks statistics for each plane. Gaps are reconstructed with a conditional stochastic process, to propagate texture global features into the damaged area, using information stored in the dictionary. Our restoration method is simple, easy and fast, with very good results for a large set of textured images. Results are compared with a state-of-the-art restoration algorithm.
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Paper Nr: 242
Title:

BINARY MORPHOLOGY AND RELATED OPERATIONS ON RUN-LENGTH REPRESENTATIONS

Authors:

Thomas M. Breuel

Abstract: Binary morphology on large images is compute intensive, in particular for large structuring elements. Runlength encoding is a compact and space-saving technique for representing images. This paper describes how to implement binary morphology directly on run-length encoded binary images for rectangular structuring elements. In addition, it describes efficient algorithm for transposing and rotating run-length encoded images. The paper evaluates and compares run length morphologial processing on page images from the UW3 database with an efficient and mature bit blit-based implementation and shows that the run length approach is several times faster than bit blit-based implementations for large images and masks. The experiments also show that complexity decreases for larger mask sizes. The paper also demonstrates running times on a simple morphology-based layout analysis algorithm on the UW3 database and shows that replacing bit blit morphology with run length based morphology speeds up performance approximately two-fold.
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Paper Nr: 298
Title:

FREE-VIEW POINT TV WATERMARKING EVALUATED ON GENERATED ARBITRARY VIEWS

Authors:

Evlambios E. Apostolidis and Georgios Triantafyllidis

Abstract: The recent advances in Image Based Rendering (IBR) has pioneered a new technology, free view point television, in which TV-viewers select freely the viewing position and angle by the application of IBR on the transmitted multi-view video. In this paper, exhaustive tests were carried out to conclude to the best possible free-view point TV watermarking evaluated on arbitrary views. The watermark should not only be extracted from a generated arbitrary view, it should also be resistant to common video processing and multiview video processing operations.
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Paper Nr: 300
Title:

MULTI-ERROR CORRECTION OF IMAGE FORMING SYSTEMS BY TRAINING SAMPLES MAINTAINING COLORS

Authors:

Gerald Krell and Bernd Michaelis

Abstract: Optical and electronic components of image forming devices degrade objective and subjective quality of the acquired or reproduced images. Classical restoration techniques usually require an explicit estimation or measurement of parameters for each error source. We propose to derive restoration parameters in a training phase with suitable test patterns for a particular system to be corrected. Space varying properties of different classes of image degradations are considered simultaneously. It is shown how training is performed in such a way that colors are reproduced correctly independently of the used test patterns.
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Paper Nr: 308
Title:

PROGRESSIVE DCT BASED IMAGE CODEC USING STATISTICAL PARAMETERS

Authors:

Pooneh B. Zadeh, Tom Buggy and Akbar Sheikh Akbari

Abstract: This paper presents a novel progressive statistical and discrete cosine transform based image-coding scheme. The proposed coding scheme divides the input image into a number of non-overlapping pixel blocks. The coefficients in each block are then decorrelated into their spatial frequencies using a discrete cosine transform. Coefficients with the same spatial frequency at different blocks are put together to generate a number of matrices, where each matrix contains coefficients of a particular spatial frequency. The matrix containing DC coefficients is losslessly coded to preserve visually important information. Matrices, which consist of high frequency coefficients, are coded using a novel statistical encoder developed in this paper. Perceptual weights are used to regulate the threshold value required in the coding process of the high frequency matrices. The coded matrices generate a number of bitstreams, which are used for progressive image transmission. The proposed coding scheme, JPEG and JPEG2000 were applied to a number of test images. Results show that the proposed coding scheme outperforms JPEG and JPEG2000 subjectively and objectively at low compression ratios. Results also indicate that the decoded images using the proposed codec have superior subjective quality at high compression ratios compared to that of JPEG, while offering comparable results to that of JPEG2000.
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Paper Nr: 312
Title:

AN APPROACH FOR SLANT CORRECTION USING PROJECTIVE TRANSFORMATION

Authors:

Abstract: Slant correction is a major challenge faced during handwritten text recognition process. Most of the traditional techniques try to estimate the global slant angle for the whole word, and rotate the word by this angle to remove the slant. On the other hand, certain other techniques estimate the slant angle at each abscissa using various techniques like DP technique. This paper presents an approach for correction of non-uniform slants in words using a hybrid of traditional global slant correction techniques and local slant approximation techniques. In this paper, we focus on correcting the slant of words that appear on check image under Legal Amount Region.

Paper Nr: 339
Title:

USE OF SPATIAL ADAPTATION FOR IMAGE RENDERING BASED ON AN EXTENSION OF THE CIECAM02

Authors:

olivier tulet, Chaker Larabi and Christine Fernandez Maloigne

Abstract: With the development and the multiplicity of imaging devices, the color quality and portability have become a very challenging problem. Moreover, a color is perceived with regards to its environment. In order to take into account the variation of perceptual vision in function of environment, the CIE (Commission Internationale de l'éclairage) has standardized a tool named color appearance model (CIECAM97*, CIECAM02). These models are able to take into account many phenomena related to human vision of color and can predict the color of a stimulus, function of its observations conditions. However, these models do not deal with the influence of spatial frequencies which can have a big impact on our perception. In this paper, an extended version of the CIECAM02 was presented. This new version integrates a spatial model correcting the color in relation to its spatial frequency and its environment. Moreover, a study on the influence of the background’s chromaticity has been also performed. The obtained results are sound and demonstrate the efficiency of the proposed extension.
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Paper Nr: 383
Title:

COLOR QUANTIZATION BY MORPHOLOGICAL HISTOGRAM PROCESSING

Authors:

Franklin C. Flores, Leonardo Facci and Roberto Lotufo

Abstract: In a previous paper it was proposed a graylevel quantization method by morphological histogram processing. This paper introduces the extension of that quantization method to color images. Considering an image under the RGB color space model, this extension reduces the number of colors in the image by partitioning an 3-D histogram, similar to the RGB color space, in rectangular parallelepiped regions, through a iterative process. Such partitioning is done, in each iteration, by application of the graylevel quantization method to the longest dimension of the current region which has the greatest volume. The final classified color space is used to quantize the image. This paper also shows the comparison of the proposed method to the classical median cut one.
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Paper Nr: 428
Title:

FINGERPRINT IMAGE SEGMENTATION BASED ON BOUNDARY VALUES

Authors:

Muhammad U. Akram, Anam Tariq, Shahida Jabeen and Shoab Ahmed Khan

Abstract: A critical step in automatic fingerprint identification system(AFIS) is the accurate segmentation of fingerprint images. The objective of fingerprint segmentation is to extract the region of interest(ROI).We present a method for fingerprint segmentation based on boundary area gray-level values. We also present a modified traditional gradient based segmentation technique. The enhanced segmentation technique is tested on FVC2004 database and results show that our modified method gives better results in all cases.
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Paper Nr: 141
Title:

DATA EVALUATION FOR DEPTH CALIBRATION OF A CUSTOMARY PMD RANGE IMAGING SENSOR CONSIDERING OBJECTS WITH DIFFERENT ALBEDO

Authors:

Jochen Radmer, Alexander Sabov and Jörg Krüger

Abstract: For various applications, such as object recognition or tracking and especially when the object is partly occluded or articulated, 3D information is crucial for the robustness of the application. A recently developed sensor to aquire distance information is based on the Photo Mixer Device (PMD) technique. This article presents an easy but accurate data acquisition method for data evaluation of a customary sensor. Data evaluation focuses on the detection of the over- and underexposured data under consideration of objects with two different albedos.
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Paper Nr: 225
Title:

HIGH-SPEED IMAGE FEATURE DETECTION USING FPGA IMPLEMENTATION OF FAST ALGORITHM

Authors:

Marek Kraft, Adam Schmidt and Andrzej Kasiński

Abstract: Many of contemporary computer and machine vision applications require finding of corresponding points across multiple images. To that goal, among many features, the most commonly used are corner points. Corners are formed by two or more edges, and mark the boundaries of objects or boundaries between distinctive object parts. This makes corners the feature points that used in a wide range of tasks. Therefore, numerous corner detectors with different properties have been developed. In this paper, we present a complete FPGA architecture implementing corer detection. This architecture is based on the FAST algorithm. The proposed solution is capable of processing the incoming image data with the speed of hundreds of frames per second for a 512×512, 8-bit gray-scale image. The speed is comparable to the results achieved by top-of-the-shelf general purpose processors. However, the use of inexpensive FPGA allows to cut costs, power consumption and to reduce the footprint of a complete system solution. The paper includes also a brief description of the implemented algorithm, resource usage summary, resulting images, as well as block diagrams of the described architecture.
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Paper Nr: 225
Title:

HIGH-SPEED IMAGE FEATURE DETECTION USING FPGA IMPLEMENTATION OF FAST ALGORITHM

Authors:

Marek Kraft, Adam Schmidt and Andrzej Kasiński

Abstract: Many of contemporary computer and machine vision applications require finding of corresponding points across multiple images. To that goal, among many features, the most commonly used are corner points. Corners are formed by two or more edges, and mark the boundaries of objects or boundaries between distinctive object parts. This makes corners the feature points that used in a wide range of tasks. Therefore, numerous corner detectors with different properties have been developed. In this paper, we present a complete FPGA architecture implementing corer detection. This architecture is based on the FAST algorithm. The proposed solution is capable of processing the incoming image data with the speed of hundreds of frames per second for a 512×512, 8-bit gray-scale image. The speed is comparable to the results achieved by top-of-the-shelf general purpose processors. However, the use of inexpensive FPGA allows to cut costs, power consumption and to reduce the footprint of a complete system solution. The paper includes also a brief description of the implemented algorithm, resource usage summary, resulting images, as well as block diagrams of the described architecture.
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Paper Nr: 276
Title:

FACE HALLUCINATION USING PCA IN WAVELET DOMAIN

Authors:

Abdu R. V. and Jiji C. V.

Abstract: The term face hallucination stands for recognition based super resolution of face images to improve the spatial resolution. In this paper, we propose two face hallucination algorithms based on principal component analysis (PCA) in the wavelet transform domain. In the spatial domain, the PCA based super resolution algorithm; a low resolution (LR) observation is represented as the linear combination of LR images in an image database. Super resolved image is obtained as the linear combination of the corresponding high resolution (HR) images in the database. In the first approach proposed in this paper, PCA based hallucination algorithm is applied to the wavelet coefficients of face image. The hallucinated face image is reconstructed from the super resolved wavelet coefficients. In second method, face image is split in to four sub images and the first method is separately applied to three textured regions. Fourth region, which is relatively smooth, is interpolated using standard interpolation techniques. We compare the performance of the two proposed algorithms with their spatial domain counter parts. The proposed method shows significant improvement over the spatial domain approaches.
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Paper Nr: 326
Title:

APPROXIMATE POINT-TO-SURFACE REGISTRATION WITH A SINGLE CHARACTERISTIC POINT

Authors:

Darko Dimitrov, Christian Knauer, Klaus Kriegel and Fabian Stehn

Abstract: We present approximation algorithms for point-to-surface registration problems which have applications in medical navigation systems. One of the central tasks of such a system is to determine a “good” mapping (the registration transformation or registration for short) of the coordinate system of the operation theatre onto the coordinate system of a 3D model M of a patient, generated from CR- or MRT scans. The registration φ is computed by matching a 3D point set P measured on the skin of the patient to the 3D model M. It is chosen from a class R of admissible transformations (e.g., rigid motions) so that it approxi- mately minimizes a suitable error function e (such as the directed Hausdorff or mean squared error distance) between ∅ (P) and M, i.e., ∅ = arg minφ′ ∈R e(φ′ (P), M). A common technique to support the registration process is to determine either automatically or manually so-called characteristic points or landmarks, which are corresponding points on the model and in the point set. Since corresponding characteristic points are supposed to be mapped onto (or close to) each other, this reduces the number of degrees of freedom of the matching problem. We provide approximation algorithms which compute a rigid motion registration in the most difficult setting of only a single characteristic point.
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Paper Nr: 362
Title:

AN AUTOMATED VISUAL EVENT DETECTION SYSTEM FOR CABLED OBSERVATORY VIDEO

Authors:

Danelle Cline, Duane Edgington and Jérôme Mariette

Abstract: This paper presents an overview of a system for processing video streams from underwater cabled observatory systems based on the Automated Visual Event Detection (AVED) software. This system identifies potentially interesting visual events using a neuromorphic vision algorithm and tracks events frame-by-frame. The events can later be previewed or edited in a graphical user interface for false detections, and subsequently imported into a database, or used in an object classification system.
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Paper Nr: 548
Title:

NEWBORN’S BIOMETRIC IDENTIFICATION: CAN IT BE DONE?

Authors:

Daniel Weingaertner, Olga Regina Pereira Bellon, Luciano Silva and Mônica Nunes Lima Cat

Abstract: In this article we propose a novel biometric identification method for newborn babies using their palmprints. A new high resolution optical sensor was developed, which obtains images with enough ridge minutiae to uniquely identify the baby. The palm and footprint images of 106 newborns were analysed, leading to the conclusion that palmprints yield more detailed images then footprints. Fingerprint experts from the Identifcation Institute of Paraná State performed two matching tests, resulting in a correct identification rate of 63.3% and 67.7%, more than three times higher than that obtained on similar experiments described on literature. The proposed image acquisition method also opens the perspective for the creation of an automatic identification system for newborns.
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Area 2 - Image Analysis

Full Papers
Paper Nr: 18
Title:

A MODEL-BASED APPROACH TO SHAPE FROM FOCUS

Authors:

Seyed V. Jalali and Mahmood Fathi

Abstract: Shape from focus (SFF) estimates the structure of a 3D object using the degree of focus as a cue in a sequence of observations. The estimate of the depth profile is however, vulnerable to lack of sufficient scene texture. In this paper, we propose a method to improve the estimate of the structure of the object by exploiting neighbourhood dependencies. A degradation model is used to describe the formation of space-variantly blurred observations in SFF. The shape of the object is modeled as a Markov random field and a suitably derived objective function is minimized to arrive at the final estimate of the shape.

Paper Nr: 53
Title:

BINARY IMAGE SKELETON - Continuous Approach

Authors:

Leonid Mestetskiy and Andrey Semenov

Abstract: In this paper we propose a building technique of a correct model of continuous skeleton for discrete binary image. Our approach is based on approximation of each connected object in an image by a polygonal figure. Figure boundary consists of closed paths of minimal perimeter which separate points of foreground and background. Figure skeleton is constructed as a locus of centers of maximal inscribed circles. In order to build a so-called skeletal base from figure skeleton, we cut unnecessary noise from it. It is shown, that the constructed continuous skeleton exists and is unique for each binary image. This continuous skeleton has the following advantages: it has a strict mathematical description, it is stable to noise, and it also has broad capabilities of form transformations and shape comparison of objects. The proposed approach gives a substantial advantage in the speed of skeleton construction in comparison with various discrete methods, including those in which parallel calculations are used. This advantage is demonstrated on real images of big size.
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Paper Nr: 55
Title:

EFFECT OF FACIAL EXPRESSIONS ON FEATURE-BASED LANDMARK LOCALIZATION IN STATIC GREY SCALE IMAGES

Authors:

Yulia Gizatdinova and Veikko Surakka

Abstract: The present aim was to examine the effect of facial expressions on the feature-based landmark localization in static grey scale images. In the method, local oriented edges were extracted and edge maps of the image were constructed at two levels of resolution. Regions of connected edges represented landmark candidates and were further verified by matching against the edge orientation model. The method was tested on a large database of expressive faces coded in terms of action units. Action units described single and conjoint facial muscle activations in upper and lower face. As results demonstrated, eye regions were located with high rates in both neutral and expressive datasets. Nose and mouth localization was more attenuated by variations in facial expressions. The present results specified some of the critical facial behaviours that should be taken into consideration while improving automatic landmark detectors which rely on the low-level edge and intensity information.
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Paper Nr: 85
Title:

A NORMALIZED PARAMETRIC DOMAIN FOR THE ANALYSIS OF THE LEFT VENTRICULAR FUNCTION

Authors:

Jaume Garcia-Barnes, Debora Gil, Sandra Pujadas, francesc carreras and Manel Ballester

Abstract: Impairment of left ventricular (LV) contractility due to cardiovascular diseases is reflected in LV motion patterns. The mechanics of any muscle strongly depends on the spatial orientation of its muscular fibers since the motion that the muscle undergoes mainly takes place along the fiber. The helical ventricular myocardial band (HVMB) concept describes the myocardial muscle as a unique muscular band that twists in space in a non homogeneous fashion. The 3D anisotropy of the ventricular band fibers suggests a regional analysis of the heart motion. Computation of normality models of such motion can help in the detection and localization of any cardiac disorder. In this paper we introduce, for the first time, a normalized parametric domain that allows comparison of the left ventricle motion across patients. We address, both, extraction of the LV motion from Tagged Magnetic Resonance images, as well as, defining a mapping of the LV to a common normalized domain. Extraction of normality motion patterns from 17 healthy volunteers shows the clinical potential of our LV parametrization.
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Paper Nr: 130
Title:

NONRIGID OBJECT SEGMENTATION AND OCCLUSION DETECTION IN IMAGE SEQUENCES

Authors:

Ketut Fundana, Niels Chr. Overgaard, Anders Heyden, David Gustavsson and Mads Nielsen

Abstract: We address the problem of nonrigid object segmentation in image sequences in the presence of occlusions. The proposed variational segmentation method is based on a region-based active contour of the Chan-Vese model augmented with a frame-to-frame interaction term as a shape prior. The interaction term is constructed to be pose-invariant by minimizing over a group of transformations and to allow moderate deformation in the shape of the contour. The segmentation method is then coupled with a novel variational contour matching formulation between two consecutive contours which gives a mapping of the intensities from the interior of the previous contour to the next. With this information occlusions can be detected and located using deviations from predicted intensities and the missing intensities in the occluded regions can be reconstructed. After reconstructing the occluded regions in the novel image, the segmentation can then be improved. Experimental results on synthetic and real image sequences are shown.
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Paper Nr: 134
Title:

LEARNING A WARPED SUBSPACE MODEL OF FACES WITH IMAGES OF UNKNOWN POSE AND ILLUMINATION

Authors:

Jihun Hamm and Daniel Lee

Abstract: In this paper we tackle the problem of learning the appearances of a person’s face from images with both unknown pose and illumination. The unknown, simultaneous change in pose and illumination makes it difficult to learn 3D face models from data without manual labeling and tracking of features. In comparison, image-based models do not require geometric knowledge of faces but only the statistics of data itself, and therefore are easier to train with images with such variations. We take an image-based approach to the problem and propose a generative model of a warped illumination subspace. Image variations due to illumination change are accounted for by a low-dimensional linear subspace, whereas variations due to pose change are approximated by a geometric warping of images in the subspace. We demonstrate that this model can be efficiently learned via MAP estimation and multiscale registration techniques. With this learned warped subspace we can jointly estimate the pose and the lighting conditions of test images and improve recognition of faces under novel poses and illuminations. We test our algorithm with synthetic faces and real images from the CMU PIE and Yale face databases. The results show improvements in prediction and recognition performance compared to other standard methods.
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Paper Nr: 137
Title:

ADDING COLOR TO GEODESIC INVARIANT FEATURES

Authors:

Pier P. Campari, Matteo Matteucci and Davide Migliore

Abstract: Geodesic invariant feature have been originally proposed to build a new local feature descriptor invariant not only to affine transformations, but also to general deformations. The aim of this paper is to investigate the possible improvements given by the use of color information in this kind of descriptor. We introduced color information both in geodesic feature construction and description. At feature construction level, we extended the fast marching algorithm to use color information; at description level, we tested several color spaces on real data and we devised the opponent color space as an useful integration to intensity information. The experiments used to validate our theory are based on publicly available data and show the improvement, in precision and recall, with respect to the original intensity based geodesic features. We also compared this kind of features, on affine and non affine transformation, with SIFT, steerable filters, moments invariants, spin images and GIH.
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Paper Nr: 146
Title:

POISSON LOCAL COLOR CORRECTION FOR IMAGE STITCHING

Authors:

Amin Sadeghi, Mohsen Hejrati and Niloofar Gheissari

Abstract: A new method for seamless image stitching is presented. The proposed algorithm is a hybrid method which uses optimal seam methods and smoothes the intensity transition between two images by color correction. A dynamic programming algorithm that finds an optimal seam along which gradient disparities are minimized is used. A modification of Poisson image editing is utilized to correct color differences between two images. Different boundary conditions for the Poisson equation were investigated and tested, and mixed boundary conditions generated the most accurate results. To evaluate and compare the proposed method with competing ones, a large image database consisting of more than two hundred image pairs was created. The test image pairs are taken at different lighting conditions, scene geometries and camera positions. On this database the proposed approach tested favorably as compared to standard methods and has shown to be very effective in producing visually acceptable images.
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Paper Nr: 149
Title:

INNER LIP SEGMENTATION BY COMBINING ACTIVE CONTOURS AND PARAMETRIC MODELS

Authors:

sebastien stillittano and Alice CAPLIER

Abstract: Lip reading applications require accurate information about lip movement and shape, and both outer and inner contours are useful. In this paper, we introduce a new method for inner lip segmentation. From the outer lip contour given by a preexisting algorithm, we use some key points to initialize an active contour called “jumping snake”. According to some optimal information of luminance and chrominance gradient, this active contour fits the position of two parametric models; a first one composed of two cubic curves and a broken line in case of a closed mouth, and a second one composed of four cubic curves in case of an open mouth. These parametric models give a flexible and accurate final inner lip contour. Finally, we present several experimental results demonstrating the effectiveness of the proposed algorithm.
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Paper Nr: 169
Title:

RECOGNITION OF DYNAMIC VIDEO CONTENTS BASED ON MOTION TEXTURE STATISTICAL MODELS

Authors:

Tomas Crivelli, Bruno Cernushi-Frias, Patrick BOUTHEMY and Jian-feng Yao

Abstract: The aim of this work is to model, learn and recognize, dynamic contents in video sequences, displayed mostly by natural scene elements, such as rivers, smoke, moving foliage, fire, etc. We adopt the mixed-state Markov random fields modeling recently introduced to represent the so-called motion textures. The approach consists in describing the spatial distribution of some motion measurements which exhibit values of two types: a discrete component related to the absence of motion and a continuous part for measurements different from zero. Based on this, we present a method for recognition and classification of real motion textures using the generative statistical models that can be learned for each motion texture class. Experiments on sequences from the DynTex dynamic texture database demonstrate the performance of this novel approach.
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Paper Nr: 258
Title:

NOVEL TECHNIQUES FOR AUTOMATICALLY ENHANCED VISUALIZATION OF CORONARY ARTERIES IN MSCT DATA AND FOR DRAWING DIRECT COMPARISONS TO CONVENTIONAL ANGIOGRAPHY

Authors:

Marion Jähne, Christina Lacalli and Stefan Wesarg

Abstract: The new generation of multi-slice computed tomography (MSCT) scanners enables the radiologist to assess the coronary arteries in a non-invasive way. The question of particular interest is whether the quality of the findings based on MSCT data can compete with the gold standard - the coronary angiography. In this work we present novel automated methods for a reliable visualization of coronary arteries and for drawing direct visual side-by-side comparisons to conventional angiograms. Our approach comprises a new method for automatically extracting the heart from cardiac CT data and an advanced masking method for eliminating large cardiac cavities to obtain a better visibility of the coronary arteries in the rendered CT data. For drawing direct side-by-side comparisons we present a novel approach for simulating the conventional coronary angiography in an easy-to-handle manner. The new methods have been developed for and tested with contrast-enhanced cardiac CT datasets.
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Paper Nr: 258
Title:

NOVEL TECHNIQUES FOR AUTOMATICALLY ENHANCED VISUALIZATION OF CORONARY ARTERIES IN MSCT DATA AND FOR DRAWING DIRECT COMPARISONS TO CONVENTIONAL ANGIOGRAPHY

Authors:

Marion Jähne, Christina Lacalli and Stefan Wesarg

Abstract: The new generation of multi-slice computed tomography (MSCT) scanners enables the radiologist to assess the coronary arteries in a non-invasive way. The question of particular interest is whether the quality of the findings based on MSCT data can compete with the gold standard - the coronary angiography. In this work we present novel automated methods for a reliable visualization of coronary arteries and for drawing direct visual side-by-side comparisons to conventional angiograms. Our approach comprises a new method for automatically extracting the heart from cardiac CT data and an advanced masking method for eliminating large cardiac cavities to obtain a better visibility of the coronary arteries in the rendered CT data. For drawing direct side-by-side comparisons we present a novel approach for simulating the conventional coronary angiography in an easy-to-handle manner. The new methods have been developed for and tested with contrast-enhanced cardiac CT datasets.
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Paper Nr: 374
Title:

HEAD POSE ESTIMATION IN FACE RECOGNITION ACROSS POSE SCENARIOS

Authors:

M.Saquib Sarfraz and Olaf Hellwich

Abstract: We present a robust front-end pose classification/estimation procedure to be used in face recognition scenarios. A novel discriminative feature description that encodes underlying shape well and is insensitive to illumination and other common variations in facial appearance, such as skin colour etc., is proposed. Using such features we generate a pose similarity feature space (PSFS) that turns the multi-class problem into two-class by using inter-pose and intra-pose similarities. A new classification procedure is laid down which models this feature space and copes well with discriminating between nearest poses. For a test image it outputs a measure of confidence or so called posterior probability for all poses without explicitly estimating underlying densities. The pose estimation system is evaluated using CMU Pose, Illumination and Expression (PIE) database.
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Paper Nr: 442
Title:

Fast Multi-View Evaluation of Data Represented by Symmetric Clusters

Authors:

Davide Moroni, Davide Moroni, Sara Colantonio and Ovidio Salvetti

Abstract: A new framework is proposed for a fast calculation of linear scalings posed on structured data. Several widely used types of data representation based on clusters with intrinsic features of local simmetry are taken into account. Paper presents some Image Mining technologies that are used for improvement of abstract data multi-view evaluation procedures.

Short Papers
Paper Nr: 14
Title:

CLUSTERED CELL SEGMENTATION - Based on Iterative Voting and the Level Set Method

Authors:

Arjan Kuijper, Yayun Zhou and Bettina Heise

Abstract: In this paper we deal with images in which the cells cluster together and the boundaries of the cells are ambiguous. Combining the outcome of an automatic point detector with the multiphase level set method, the centre of each cell is detected and used as the ”seed”, in other words, the initial condition for level set method. Then by choosing appropriate level set equation, the fronts of the seeds propagate and finally stop near the boundary of the cells. This method solves the cluster problem and can distinguish individual cells properly, therefore it is useful in cell segmentation. By using this method, we can count the number of the cells and calculate the area of each cell. Furthermore, this information can be used to get the histogram of the cell image.
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Paper Nr: 16
Title:

CORNER DETECTION WITH MINIMAL EFFORT ON MULTIPLE SCALES

Authors:

Ernst Dickmanns

Abstract: Based on results of fitting linearly shaded blobs to rectangular image regions a new corner detector has been developed. A plane with least sum of errors squared is fit to the intensity distribution within a mask having four mask elements of same rectangular shape and size. Averaged intensity values in these mask elements allow very efficient simultaneous computation of pyramid levels and a new corner criterion at the center of the mask on these levels. The method is intended for real-time application and has thus been designed for minimal computing effort. It nicely fits into the ‘Unified Blob-edge-corner Method’ (UBM) developed recently. Results are given for road scenes.
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Paper Nr: 45
Title:

A NOVEL CHAOTIC CODING SYSTEM FOR LOSSY IMAGE COMPRESSION

Authors:

Sebastiano Battiato and Francesco Rundo

Abstract: In this paper a novel image compression pipeline, by making use of a controlled chaotic system, is proposed. Chaos is a particular dynamic generated by nonlinear systems. Under certain conditions it is possible to properly manage the chaotic dynamics obtaining very feasible and powerful working instruments. In the proposed compression pipeline a linear feedback control strategy has been used to stabilize chaotic dynamic used to track the 1D signal generated by the input image. The pipeline is closed by an entropy encoder. Preliminary experiments and comparison with respect to standard JPEG engine confirm the effectiveness of the proposed chaotic coding system both for natural and graphic images. Also the overall performances in terms of rate-distortion capabilities are promising.
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Paper Nr: 47
Title:

ROBUST ESTIMATION OF THE PAN-ZOOM PARAMETERS FROM A BACKGROUND AREA IN CASE OF A CRISS-CROSSING FOREGROUND OBJECT

Authors:

Jan Bruijns

Abstract: In the field of video processing, a model of the background motion has application in deriving depth from motion. The pan-zoom parameters of our background model are estimated from the motion vectors of parts which are a priori likely to belong to the background, such as the top and side borders (”the background area”). This fails when a foreground object obscures the greater part of this background area. We have developed a method to extract a set of pan-zoom parameters for each different part of the background area. Using the pan-zoom parameters of the previous frame, we compute from these sets the pan-zoom parameters most likely to correspond to the proper background parts. This background area partition method gives more accurate pan parameters for shots with the greater part of the background area obscured by one or more foreground objects than application of the entire background area.
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Paper Nr: 80
Title:

4D WARPING FOR ANALYSING MORPHOLOGICAL CHANGES IN SEED DEVELOPMENT OF BARLEY GRAINS

Authors:

Rainer Pielot, Udo Seiffert, Bertram Manz, Diana Weier, Frank Volke and Winfriede Weschke

Abstract: NMR imaging allows to obtain 3D-images by non-invasive treatment of biological structures. In this study intensity-based warping is evaluated by comparing it to landmark-based warping for a four-dimensional analysis of morphological changes in seed development of barley. The datasets of barley grains are obtained at certain development stages by NMR. Warping algorithms reconstruct intermediate physically nonmeasured stages. The landmark-based procedure consists of automatic definition of landmarks and subsequent distance-weighted warping. The intensity-based approach uses iterative intensity-based warping for definition of the displacement vector field and distance-weighted volume warping for generation of the virtual intermediate dataset. The approaches were tested with four datasets of barley at different development stages. As a result, the intensity-based approach is highly applicable for analysis of morphological changes in NMR datasets and serves as a tool for an extensive 4D analysis of seed development in barley grains.
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Paper Nr: 93
Title:

FAST AND ROBUST LOCALIZATION OF THE HEART IN CARDIAC MRI SERIES - A Cascade of Operations for Automatically Detecting the Heart in Cine MRI Series

Authors:

Sebastian Zambal, Andreas Schöllhuber, Katja Bühler and Jiří Hladůvka

Abstract: This work presents a robust approach for fast initialization of an Active Appearance Model for subsequent segmentation of cardiac MRI data. The method automatically determines AAM initialization parameters: position, orientation, and scaling of the model. Four steps are carried out: (1) variance images over time are calculated to find a bounding box that roughly defines the heart region; (2) circle Hough-transformation adapted to gray values is performed to detect the left ventricle; (3) thresholding is carried out to determine the orientation of the heart; (4) the optimal initialization is selected using a mean texture model. The method was evaluated on 42 MRI short axis studies coming from two MRI scanners of two different vendors. Automatic initializations are compared to manual ones. It is shown that the proposed automatic method is much faster than and achieves results qualitatively equal to manual initialization.
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Paper Nr: 108
Title:

MODEL BASED GLOBAL IMAGE REGISTRATION

Authors:

Niloofar Gheissari, Mostafa Kamali, Parisa Mirshams and Zohreh Sharafi

Abstract: In this paper, we propose a model-based image registration method capable of detecting the true transformation model between two images. We incorporate a statistical model selection criterion to choose the true underlying transformation model. Therefore, the proposed algorithm is robust to degeneracy as any degeneracy is detected by the model selection component. In addition, the algorithm is robust to noise and outliers since any corresponding pair that does not undergo the chosen model is rejected by a robust fitting method adapted from the literature. Another important contribution of this paper is evaluating a number of different model selection criteria for image registration task. We evaluated all different criteria based on different levels of noise. We conclude that CAIC and GBIC slightly outperform other criteria for this application. The next choices are GIC, SSD and MDL. Finally, we create panorama images using our registration algorithm. The panorama images show the success of this algorithm.
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Paper Nr: 116
Title:

DISPLAY REGISTRATION FOR DEVICE INTERACTION - A Proof of Principle Prototype

Authors:

Nick Pears, Patrick Olivier and Dan Jackson

Abstract: A method is proposed to facilitate visually-driven interactions between two devices, which we call the client, such as a mobile phone or personal digital assistant (PDA), which must be equipped with a camera, and the server, such as a personal computer (PC) or intelligent display. The technique that we describe here requires a camera on the client to view the display on the server, such that either the client or the server (or both) can compute exactly which part of the server display is being viewed. The server display and the clients image of the server display, which can be written onto (part of) the client’s display are then registered. This basic principle, which we call “display registration” supports a very broad range of interactions (depending on the context in which the system is operating) and it will make these interactions significantly quicker, easier and more intuitive for the user to initiate and control. In addition, either the client or the server (or both) can compute the six degree-of-freedom (6 DOF) position of the client camera with respect to the server display. We have built a prototype which proves the principle and usefulness of display registration. This system employs markers on the server display for fast registration and it has been used to demonstrate a variety of operations, such as selecting and zooming into images.
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Paper Nr: 131
Title:

ESTIMATION OF FACIAL EXPRESSION INTENSITY BASED ON THE BELIEF THEORY

Authors:

GHANEM KHADOUDJA, CAPLIER ALICE and Sébastien Stillittano

Abstract: This article presents a new method to estimate the intensity of a human facial expression. Supposing an expression occurring on a face has been recognized among the six universal emotions (joy, disgust, surprise, sadness, anger, fear), the estimation of the expression’s intensity is based on the determination of the degree of geometrical deformations of some facial features and on the analysis of several distances computed on skeletons of expressions. These skeletons are the result of a contour segmentation of facial permanent features (eyes, brows, mouth). The proposed method uses the belief theory for data fusion. The intensity of the recognized expression is scored on a three-point ordinal scale: "low intensity", "medium intensity" or " high intensity". Experiments on a great number of images validate our method and give good estimation for facial expression intensity. We have implemented and tested the method on the following three expressions: joy, surprise and disgust.
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Paper Nr: 178
Title:

A FAST AND ROBUST METHOD FOR VOLUMETRIC MRI BRAIN EXTRACTION

Authors:

Sami Bourouis and Kamel Hamrouni

Abstract: This paper presents a method for magnetic resonance imaging (MRI) segmentation and the extraction of main brain tissues. The method uses an image processing technique based on level-set approach and EM-algorithm. The paper describes the main features of the method, and presents experimental results with real volumetric images in order to evaluate the performance of the method.
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Paper Nr: 179
Title:

MULTIRESOLUTION MESH SEGMENTATION OF MRI BRAIN USING CLASSIFICATION AND DISCRETE CURVATURE

Authors:

Sami Bourouis, Kamel Hamrouni and Mounir Dhibi

Abstract: This paper presents a method for brain tissue segmentation and characterization of magnetic resonance imaging (MRI) scans. It is based on statistical classification, differential geometry, and multiresolution representation. The Expectation Maximization algorithm and k-means clustering are applied to generate an initial mask of tissue classes of data volume. Then, a hierarchical multiresolution representation is applied to simplify processing. The idea is that the low-resolution description is used to determine constraints for the segmentation at the higher resolutions. Our contribution is the design of a pipeline procedure for brain characterization/labeling by using discrete curvature and multiresolution representation. We have tested our method on several MRI data.
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Paper Nr: 246
Title:

ENHANCED PHASE–BASED DISPLACEMENT ESTIMATION - An Application to Facial Feature Extraction and Tracking

Authors:

Mohamed Dahmane, Jean Meunier and Jean Meunier

Abstract: In this work, we develop a multi-scale approach for automatic facial feature detection and tracking. The method is based on a coarse to fine paradigm to characterize a set of facial fiducial points using a bank of Gabor filters that have interesting properties such as directionality, scalability and hierarchy. When the first face image is captured, a trained grid is used on the coarsest level to estimate a rough position for each facial feature. Afterward, a refinement stage is performed from the coarsest to the finest (original) image level to get accurate positions. These are then tracked over the subsequent frames using a modification of a fast phase– based technique. This includes a redefinition of the confidence measure and introduces a conditional disparity estimation procedure. Experimental results show that facial features can be localized with high accuracy and that their tracking can be kept during long periods of free head motion.
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Paper Nr: 297
Title:

DEPTH-BASED DETECTION OF SALIENT MOVING OBJECTS IN SONIFIED VIDEOS FOR BLIND USERS

Authors:

Benoît Deville, Guido Bologna, Michel Vinckenbosch and Thierry Pun

Abstract: The context of this work is the development of a mobility aid for visually impaired persons. We present here an original approach for a real time alerting system, based on the use of feature maps for detecting visual salient parts in images. In order to improve the quality of this method, we propose here to benefit from a new feature map constructed from the depth gradient. A specific distance function is described, which takes into account both stereoscopic camera limitations and users choices. We demonstrate here that this additional depth-based feature map allows the system to detect the salient regions with good accuracy in most situations, even with noisy disparity maps.
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Paper Nr: 306
Title:

EVALUATION OF LOCAL ORIENTATION FOR TEXTURE CLASSIFICATION

Authors:

Dana I. Ghita, Ovidiu Ghita and Paul F. Whelan

Abstract: The aim of this paper is to present a study where we evaluate the optimal inclusion of the texture orientation in the classification process. In this paper the orientation for each pixel in the image is extracted using the partial derivatives of the Gaussian function and the main focus of our work is centred on the evaluation of the local dominant orientation (which is calculated by combining the magnitude and local orientation) on the classification results. While the dominant orientation of the texture depends strongly on the observation scale, in this paper we propose to evaluate the macro-texture by calculating the distribution of the dominant orientations for all pixels in the image that sample the texture at micro-level. The experimental results were conducted on standard texture databases and the results indicate that the dominant orientation calculated at micro-level is an appropriate measure for texture description.
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Paper Nr: 314
Title:

MEAN SHIFT SEGMENTATION - Evaluation of Optimization Techniques

Authors:

Jens Kaftan, Andre Bell and Til Aach

Abstract: The mean shift algorithm is a powerful clustering technique, which is based on an iterative scheme to detect modes in a probability density function. It has been utilized for image segmentation by seeking the modes in a feature space composed of spatial and color information. Although the modes of the feature space can be efficiently calculated in that scheme, different optim zation techniques have been investigated to further improve the calculation speed. Besides those techniques that improve the efficiency using specialized data structures, there are other ones, which take advantage of some heuristics, and therefore affect the accuracy of the algorithm output. In this paper we discuss and evaluate different optimization strategies for mean shift based image segmentation. These optimization techniques are quantitatively evaluated based on different real world images. We compare segmentation results of heuristic-based, performance-optimized implementations with the segmentation result of the original mean shift algorithm as a gold standard. Towards this end, we utilize different partition distance measures, by identifying corresponding regions and analyzing the thus revealed differences.
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Paper Nr: 316
Title:

A ROBUST AND EFFICIENT METHOD FOR TOPOLOGY ADAPTATIONS IN DEFORMABLE MODELS

Authors:

Jochen Abhau

Abstract: In this paper, we present a novel algorithm for calculating topological adaptations in explicit evolutions of surface meshes in 3D. Our topological adaptation system consists of two main ingredients: A spatial hashing technique is used to detect mesh self-collisions during the evolution. Its expected running time is linear with respect to the number of vertices. A database consisting of possible topology changes is developed in the mathematical framework of homology theory. This database allows for fast and robust topology adaptation during a mesh evolution. The algorithm works without mesh reparametrizations, global mesh smoothness assumptions or vertex sampling density conditions, making it suitable for robust, near real-time application. Furthermore, it can be integrated into existing mesh evolutions easily. Numerical examples from medical imaging are given.
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Paper Nr: 320
Title:

ESTIMATING CAMERA ROTATION PARAMETERS FROM A BLURRED IMAGE

Authors:

Giacomo Boracchi, Vincenzo Caglioti and Alberto Danese

Abstract: A fast rotation of the camera during the image acquisition results in a blurred image, which typically shows curved smears. We propose a novel algorithm for estimating both the camera rotation axis and the camera angular speed from a single blurred image. The algorithm is based on local analysis of the blur smears. Contrary to the existing methods, we treat the more general case where the rotation axis can be not orthogonal to the image plane, taking into account the perspective effects that in such case affect the smears. The algorithm is validated in experiments with synthetic and real blurred images, providing accurate estimates.
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Paper Nr: 333
Title:

LATTICE EXTRACTION BASED ON SYMMETRY ANALYSIS

Authors:

Manuel Agustí-Melchor, Jose Valiente and Ángel Rodas

Abstract: In many computer tasks it is necessary to structurally describe the contents of images for further processing, for example, in regular images produced in industrial processes such as textiles or ceramics. After reviewing the different approaches found in the literature, this work redefines the problem of periodicity in terms of the existence of local symmetries. Phase symmetry analysis is chosen to obtain these symmetries because of its robustness when dealing with image contrast and noise. Also, the multiresolution nature of the technique offers independence from using fixed thresholds to segment the image. Our adaptation of the original technique, based on lattice constraints, has result in a parameter free algorithm for determining the lattice. It offers a significant increase in computational speed with respect to the original proposal. Given that there is no set of images for assessing this type of techniques, various sets of images have been used, and the results are apresented. A measure to enable the evaluation of results is also introduced, so that each calculated lattice can be tagged with an index regarding its correctness. The experiments show that using this statistic, good results are reported from image collections. Possible applications of the lattice extraction are suggested.
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Paper Nr: 341
Title:

CHARACTERISATION AND AUTOMATIC DETECTION OF LYMPH NODES ON MR COLORECTAL IMAGES

Authors:

Jeong-Gyoo Kim and Michael Brady

Abstract: Colorectal cancer is the second most common cause of death in Western countries. It is often curable by chemoradiotherapy and/or surgery; however, accurate staging has a significant impact on patient management and outcome. Numerous clinical reports attest to the fact that staging is not currently satisfactory, and so more precise methods are required for effective treatment. The three major components of disease staging are tumour size; whether or not there is distal metastatic spread; and the extent of lymph node involvement. Of these, the latter is currently by far the hardest to quantify, and it is the subject of this paper. Lymph nodes are distributed throughout the mesorectal fascia that envelops the colorectum. In practice, they are detected and assessed by clinicians using properties such as their size and shape. We are not aware of any previous image analysis approach for colorectal images that makes this subjective approach more scientific. To aid precise staging and surgery, we have developed methods that characterises lymph nodes by extracting implicit properties as computed from magnetic resonance colorectal images. We first learn the probability density function (PDF) of the intensities of the mesorectal fascia and find that it closely approximates a Gaussian distribution. The parameters of a Gaussian, fitted to the PDF, were estimated and the mean intensity of a lymph node candidate was compared with it. The fitting provides an explicit criterion for a region to be classed as a lymph node: namely, it is an outlier of the Gaussian distribution. As a key part of this process, we need to segment the boundaries of the mesorectal fascia, which is enclosed by two closed contours. Clinicians recognise the outer contour as thin edges. Since the thin edges are often ambiguous and disconnected, differentiating them from neighbouring tissues is a non-trivial problem; the surrounding tissues have no significant difference from the mesorectal fascia in both intensity and texture. We employed a level set method to segment three sets of objects: the mesorectal fascia, the colorectum, and lymph node candidates. Our segmentation results led us to build a PDF and to use it for the criterion that we propose. The whole process of implementation of our methods is automatic including the lookup of lymph candidates. The results of clinical cases are summarised in the paper.
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Paper Nr: 356
Title:

SPECKLE MODELIZATION IN OCT IMAGES FOR SKIN LAYERS SEGMENTATION

Authors:

Ali Mcheik, Clovis Tauber, Hadj Batatia, Jerome George and Jean-Michel Lagarde

Abstract: In dermatology, the optical coherence tomography (OCT) is used to visualize the skin over a few millimetre depth. These images are affected by speckle, which can alter the interpretation, but which also carry information that characterizes locally the visualized tissue. In this paper, we present a statistical study of the speckle distribution in OCT images. The capability of three probability density functions (pdf) (Rayleigh, Lognormal, and Nakagami) to differentiate the speckle distribution according to the skin layer is analysed. For each pdf, the vector of parameters, estimated over several images which are annotated by experts, are mapped onto a parameter space. Quantitative results over 30 images are compared to the manual delineations of 5 experts. Results confirm the potential of the method for the segmentation of the layers of the skin.
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Paper Nr: 369
Title:

PROJECTIVE IMAGE ALIGNMENT BY USING ECC MAXIMIZATION

Authors:

Georgios Evangelidis and Emmanouil Psarakis

Abstract: Nonlinear projective transformation provides the exact number of desired parameters to account for all possible camera motions thus making its use in problems where the objective is the alignment of two or more image profiles to be considered as a natural choice. Moreover, the ability of an alignment algorithm to quickly and accurately estimate the parameter values of the geometric transformation even in cases of over-modelling of the warping process constitutes a basic requirement to many computer vision applications. In this paper the appropriateness of the Enhanced Correlation Coefficient (ECC) function as a performance criterion in the projective image registration problem is investigated. Since this measure is a highly nonlinear function of the warp parameters, its maximization is achieved by using an iterative technique. The main theoretical results concerning the nonlinear optimization problem and an efficient approximation leading to an optimal closed form solution (per iteration) are presented. The performance of the iterative algorithm is compared against the well known Lucas-Kanade algorithm with the help of a series of experiments involving strong or weak geometric deformations, ideal and noisy conditions and even over-modelling of the warping process. In all cases ECC based algorithm exhibits a better behavior in speed, as well as in the probability of convergence as compared to the Lucas-Kanade scheme.
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Paper Nr: 370
Title:

PERFORMANCE EVALUATION OF ROBUST MATCHING MEASURES

Authors:

Federico Tombari, Luigi Di Stefano, Stefano Mattoccia and Angelo Galanti

Abstract: This paper is aimed at evaluating the performances of different measures which have been proposed in literature for robust matching. In particular, classical matching metrics typically employed for this task are considered together with specific approaches aiming at achieving robustness. The main aspects assessed by the proposed evaluation are robustness with respect to photometric distortions, noise and occluded patterns. Specific datasets have been used for testing, which provide a very challenging framework for what concerns the considered disturbance factors and can also serve as testbed for evaluation of future robust visual correspondence measures.
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Paper Nr: 380
Title:

IMAGE RE-SEGMENTATION - A New Approach Applied to Urban Imagery

Authors:

Thales S. Korting, Leila Fonseca, Luciano V. Dutra and Felipe Castro da Silva

Abstract: This article presents a new approach for image segmentation applied to urban imagery. The proposed method is called re-segmentation because it uses a previous over-segmented image as input to generate a new set of objects more adequate to the application of interest. For urban objects such as roofs, building and roads, the algorithm tries to generate rectangular objects by merging and cutting operations in a weighted Region Adjacency Graph. Objects whose union generate larger regular objects are merged or otherwise cut. In order to verify the potential of the method, two experimental results using Quickbird images are presented.
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Paper Nr: 385
Title:

SURFACE DEFECTS DETECTION ON ROLLED STEEL STRIPS BY GABOR FILTERS

Authors:

Roberto Medina, Fernando Gayubo, Luis M. González, David Olmedo, Jaime Gómez, Eduardo Zalama and José R. Perán

Abstract: Product material integrity and surface appearance, in steel flat products manufacturing and processing, are important attributes that will affect product operation, reliability and customer confidence. Automated visual inspection has to be envisaged, but five major problems have to be overcome: (i) The variable nature of the defects, (ii) The high reflective nature of the metallic surfaces, (iii) The oil presence, (iv) The huge amount of visual data to be acquired and processed, and (v) The high speed in the section where inspections are performed. We have developed an automated cellular visual inspection system of flat products in a flat steel cutting factory. Among the approaches that the system uses to detect defects, we have included the two-dimensional Gabor filters. In this paper a detection procedure of defects in flat steel products based on Gabor filters is presented. The traditional methods based on the study of the grey-level histogram and shape analysis, have shown quite good results, but there are not good enough to achieve the level of success required. Experimental results show that a greater number of defects can be readily detected using the proposed approach.
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Paper Nr: 429
Title:

CORE POINT DETECTION USING FINE ORIENTATION FIELD ESTIMATION

Authors:

Muhammad U. Akram, Rabia Arshaf, Rabia Anwar, Shoab Ahmed Khan and Sarwat Nasir

Abstract: Performance of Automatic Fingerprint Identification System( AFIS) is greatly influenced by the detection of core point. Extraction of best Region Of Interest(ROI) from image can play a vital role for core point detection. In this paper, we present an improved technique for fine orientation field estimation and core point detection. The distinct feature of our technique is that it gives high detection percentage of core point even in case of low quality fingerprint images. The proposed algorithm is applied on FVC2004 database. Results of experiments demonstrate improved performance for detecting core point.
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Paper Nr: 430
Title:

FACIAL EXPRESSION RECOGNITION BASED ON FUZZY LOGIC

Authors:

Muhammad U. Akram, Irfan Zafar, Wasim Siddique Khan and Zohaib Mushtaq

Abstract: We present a novel scheme for facial expression recognition from facial features using Mamdani-type fuzzy system. Facial expression recognition is of prime importance in human-computer interaction systems (HCI). HCI has gained importance in web information systems and e-commerce and certainly has the potential to reshape the IT landscape towards value driven perspectives. We present a novel algorithm for facial region extraction from static image. These extracted facial regions are used for facial feature extraction. Facial features are fed to a Mamdani-type fuzzy rule based system for facial expression recognition. Linguistic models employed for facial features provide an additional insight into how the rules combine to form the ultimate expression output. Another distinct feature of our system is the membership function model of expression output which is based on different psychological studies and surveys. The validation of the model is further supported by the high expression recognition percentage.
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Paper Nr: 4
Title:

SEMI-SUPERVISED DIMENSIONALITY REDUCTION USING PAIRWISE EQUIVALENCE CONSTRAINTS

Authors:

Hakan Cevikalp, Jakob Verbeek, Frédéric Jurie and Alexander Klaser

Abstract: To deal with the problem of insufficient labeled data, usually side information – given in the form of pairwise equivalence constraints between points – is used to discover groups within data. However, existing methods using side information typically fail in cases with high-dimensional spaces. In this paper, we address the problem of learning from side information for high-dimensional data. To this end, we propose a semi-supervised dimensionality reduction scheme that incorporates pairwise equivalence constraints for finding a better embedding space, which improves the performance of subsequent clustering and classification phases. Our method builds on the assumption that points in a sufficiently small neighborhood tend to have the same label. Equivalence constraints are employed to modify the neighborhoods and to increase the separability of different classes. Experimental results on high-dimensional image data sets show that integrating side information into the dimensionality reduction improves the clustering and classification performance.
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Paper Nr: 107
Title:

CONTENT-BASED SHAPE RETRIEVAL USING DIFFERENT AFFINE SHAPE DESCRIPTORS

Authors:

Fatma CHAKER, Faouzi Ghorbel and Mohamed Tarak BANNOUR

Abstract: Shape representation is a fundamental issue in the newly emerging multimedia applications. In the Content Based Image Retrieval (CBIR), shape is an important low level image feature. Many shape representations have been proposed. However, for CBIR, a shape representation should satisfy several properties such as affine invariance, robustness, compactness, low computation complexity and perceptual similarity measurement. Against these properties, in this paper we attempt to study and compare three shape descriptors: two issued from Fourier method and the Affine Curvature Scale Space Descriptor (ACSSD). We build a retrieval framework to compare shape retrieval performance in terms of robustness and retrieval performance. The retrieval performance of the different descriptors is compared using two standard shape databases. Retrieval results are given to show the comparison.
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Paper Nr: 133
Title:

ENTROPY-BASED SALIENCY COMPUTATION IN LOG-POLAR IMAGES

Authors:

Nadia Tamayo and V. Javier Traver

Abstract: Visual saliency provides a filtering mechanism to focus on a set of interesting areas in the scene, but these mechanisms often overload the computational resources of many computer vision tasks. In order to reduce such an overload and improve the computational performance, we propose to exploit the advantages of log-polar vision to detect salient regions with economy of computational resources and quite stable results. Particularly, in this paper we study the application of the entropy-based saliency to log-polar images. Some interesting considerations are presented in reference to the concept of “scale” and the effects of space-variant sampling on scale selection. We also propose a necessary border extension to detect objects present in peripheral areas. The original entropy-based saliency algorithm can be used in log-polar images, but the results show that our adaptations allow to detect with more precision log-polar salient forms because they consider the information redundancy of space-variant sampling. Compared with cartesian, log-polar salient results allow a significant saving of computational resources.
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Paper Nr: 133
Title:

ENTROPY-BASED SALIENCY COMPUTATION IN LOG-POLAR IMAGES

Authors:

Nadia Tamayo and V. Javier Traver

Abstract: Visual saliency provides a filtering mechanism to focus on a set of interesting areas in the scene, but these mechanisms often overload the computational resources of many computer vision tasks. In order to reduce such an overload and improve the computational performance, we propose to exploit the advantages of log-polar vision to detect salient regions with economy of computational resources and quite stable results. Particularly, in this paper we study the application of the entropy-based saliency to log-polar images. Some interesting considerations are presented in reference to the concept of “scale” and the effects of space-variant sampling on scale selection. We also propose a necessary border extension to detect objects present in peripheral areas. The original entropy-based saliency algorithm can be used in log-polar images, but the results show that our adaptations allow to detect with more precision log-polar salient forms because they consider the information redundancy of space-variant sampling. Compared with cartesian, log-polar salient results allow a significant saving of computational resources.
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Paper Nr: 148
Title:

A FRAMEWORK FOR ANALYZING TEXTURE DESCRIPTORS

Authors:

Timo Ahonen and Matti Pietikäinen

Abstract: This paper presents a new unified framework for texture descriptors such as Local Binary Patterns (LBP) and Maximum Response 8 (MR8) that are based on histograms of local pixel neighborhood properties. This framework is enabled by a novel filter based approach to the LBP operator which shows that it can be seen as a special filter based texture operator. Using the proposed framework, the filters to implement LBP are shown to be both simpler and more descriptive than MR8 or Gabor filters in the texture categorization task. It is also shown that when the filter responses are quantized for histogram computation, codebook based vector quantization yields slightly better results than threshold based binning at the cost of higher computational complexity.
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Paper Nr: 183
Title:

WAVELET TRANSFORM FOR PARTIAL SHAPE RECOGNITION USING SUB-MATRIX MATCHING

Authors:

El-hadi zahzah

Abstract: In this paper, we propose a method for 2D partial shape recognition under affine transform using the discrete dyadic wavelet transform invariant to translation well known as Stationary Wavelet Transform or SWT. The method we propose here is about partial shape matching and is based firstly on contour representation using the wavelet transform. A technique of sub matrix matching is then used to match partial shapes. The representation is based on three steps, the contour is first parameterized by enclosed area, the affine invariant feature is then calculated to finally determine the natural axis which enable to fix the starting point. The knowledge of the orientation of the natural axis enables to adjust the starting point on the contour between the query and the models in a given database. Furthermore, the method can selects a subset of useful invariant features for the matching step. A sub-matrix matching algorithm developed by (Saber et al., 2005)is then used to determine correspondences for evaluation of partial similarity between an example template and a candidate object region. The method is tested on a database of 5000 fish species, and the results are very satisfactory.
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Paper Nr: 198
Title:

INDEX, MIDDLE, AND RING FINGER EXTRACTION AND IDENTIFICATION BY INDEX, MIDDLE, AND RING FINGER OUTLINES

Authors:

Ching-Liang Su

Abstract: In this study, the new technique is used to extract the index, middle and ring finger outlines. The orientations and geometrical features of these outlines are calculated and compared to identify different individuals. The techniques of database SQL searching and manipulation, image dilation, object position locating, image shifting, rotation, and interpolation are used to recognize different individuals. The hand was fixed each time when a photograph was taken, and one can assume that each time when a hand was acquired, the image was the same as the previous one. Since the photographs are the same, after the index, middle or ring fingers have been extracted from the hand image, the acquired images can be used to identify different persons.
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Paper Nr: 203
Title:

IMAGE PROCESSING IN MATERIAL ANALYSES OF ARTWORKS

Authors:

Miroslav Beneš, Barbara Zitová, Janka Hradilová and David Hradil

Abstract: In this paper we present a system for processing, description and archiving material analyses used during art restoration - Nephele. The aim of the material analyses of painting layers is to identify inorganic and organic compounds using microanalytical methods, and to describe stratigraphy and morphology of layers. The results are used to interpret the applied painting technique. The Nephele system is a database system for material analysis reports, extended with image preprocessing modules and image retrieval facility. The implemented digital image processing methods are image registration, layers segmentation, and grains segmentation. In the archiving part of the Nephele, in addition to traditional database functions we have incorporated image-based retrieval methods into the developed system. They are based on feature descriptions such as the Haralick descriptors of co-occurrence matrices. The presented examples of achieved results show the applicability of the system.
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Paper Nr: 212
Title:

CONTENT-BASED IMAGE RETRIEVAL USING GENERIC FOURIER DESCRIPTOR AND GABOR FILTERS

Authors:

Quan He, ZhengQiao Ji and Jonathan Wu

Abstract: Content-based image retrieval (CBIR) is an important research area with application to large amount image databases and multimedia information. CBIR has three general visual contents, including color, texture and shape. The focus of this paper is on the problem of shape and texture feature extraction and representation for CBIR. We apply Generic Fourier Descriptor (GFD) for shape feature extraction and Gabor Filters (GF) for texture feature extraction, and we successfully combine GFD and GF together for shape and texture feature extraction. Experimental results show that the proposed GFD+GF is robust to all the test databases with best retrieval rate.
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Paper Nr: 233
Title:

ON THE IMPROVEMENT OF THE TOPOLOGICAL ACTIVE VOLUMES MODEL - A Tetrahedral Approach

Authors:

Noelia Barreira Rodríguez, Manuel G. Penedo, M. Ortega and José Rouco Maseda

Abstract: The Topological Active Volumes model is a 3D active model focused on segmentation and reconstruction tasks. The segmentation process is based on the adjustment of a 3D mesh composed of polyhedra. This adjustment is guided by the minimisation of several energy functions related to the mesh. Even though the original cubic mesh achieves good segmentation results, it has difficulties in some cases due to its shape. This paper proposes a new topology for the TAV mesh based on tetrahedra that overcomes the cubic mesh difficulties. Also, the paper explains an improvement in the tetrahedral topology to increase the accuracy of the results as well as the efficiency of the overall process.
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Paper Nr: 243
Title:

MULTIREGION GRAPH CUT IMAGE SEGMENTATION

Authors:

Mohamed BEN SALAH, Ismail BEN AYED and Amar Mitiche

Abstract: The purpose of our study is two-fold: (1) investigate an image segmentation method which combines parametric modeling of the image data and graph cut combinatorial optimization and, (2) use a prior which allows the number of labels/regions to decrease when the number of regions is not known and the algorithm initialized with a larger number. Experimental verification shows that the method results in good segmentations and runs faster than conventional graph cut methods.
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Paper Nr: 244
Title:

ACTIVE APPEARANCE MODEL (AAM) - From Theory to Implementation

Authors:

Nikzad B. Rizvandi, Aleksandra Pizurica and Wilfried Philips

Abstract: Active Appearance Model (AAM) is a kind of deformable shape descriptors which is widely used in computer vision and computer graphics. This approach utilizes statistical model obtained from some images in training set and gray-value information of the texture to fit on the boundaries of a new image. In this paper, we describe a brief implementation, apply the method on hand object and finally discuss its performance in compare to Active Shape Model(ASM). Our experiments shows this method is more sensitive to the initialization and slower than ASM.
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Paper Nr: 299
Title:

REDUCING THE EFFECT OF PARTIAL OCCLUSIONS ON IRIS RECOGNITION

Authors:

Meryem Erbilek and Onsen Toygar

Abstract: The difficulty in the process of human identification by iris recognition is that the iris images captured may have occlusions by the eyelids and eyelashes. In that case, recognition of occluded iris patterns becomes hard and the corresponding person may not be correctly recognized. In order to reduce the effect of eyelid or eyelash occlusion on the recognition of human beings by their iris patterns, we propose a simple and efficient method for iris recognition using specific regions on the iris images without using the traditional preprocessing approach before applying the feature extraction method to recognize the irises. First of all, these regions are individually experimented and then the outputs of each region are combined using a multiple classifier combination method with the feature extraction method Principal Component Analysis (PCA). The experiments on the iris images, with and without occlusions, demonstrate that the proposed approach achieves better recognition rates compared to the recognition rates of the holistic approaches.
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Paper Nr: 307
Title:

AUTOMATIC SHOT BOUNDARY DETECTION USING GAUSSIAN MIXTURE MODEL

Authors:

Adhipathi R. Aleti and Sridhar Varadarajan

Abstract: The basic step for video analysis is the detection of shots in a given video. A shot is sequence of frames captured in a single continuous action in time and space using a single camera. The boundary between two adjacent shots may be an abrupt change (hard cut) or gradual change. In literature, many shot boundary detection algorithms have been proposed for detecting the hard cut or gradual changes like fadein/out and dissolve. The performance of these algorithms degrades with zooming, lighting change conditions, and fast moving type of videos. In this paper, a novel algorithm based on Gaussian Mixture Model (GMM) is developed for shot boundary detection. The behavior of GMM with abrupt and gradual change is used for detection of hard cut, fadein/out and dissolve. Experimental results shows credibility of the proposed algorithm with zooming, lighting change conditions, and fast moving type of videos.
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Paper Nr: 335
Title:

BUILDING A NORMALITY SPACE OF EVENTS - A PCA Approach to Event Detection

Authors:

Angelo Cenedese, Ruggero Frezza, Enrico Campana, Giambattista Gennari and Giambattista Gennari

Abstract: The detection of events in video streams is a central task in the automatic vision paradigm, and spans heterogeneous fields of application from the surveillance of the environment, to the analysis of scientific data. Actually, although well captured by intuition, the definition itself of event is somewhat hazy and depending on the specific application of interest. In this work, the approach to the problem of event detection is different in nature. Instead of defining the event and searching for it within the data, a normality space of the scene is built from a chosen learning sequence The event detection algorithm works by projecting any newly acquired image onto the normality space so as to calculate a distance from it that represents the innovation of the new frame, and defines the metric for triggering an event alert.
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Paper Nr: 338
Title:

A SUBJECTIVE SURFACES BASED SEGMENTATION FOR THE RECONSTRUCTION OF BIOLOGICAL CELL SHAPE

Authors:

Matteo Campana, Cecilia Zanella, Barbara Rizzi, Paul Bourgine, Nadine Peyrieras and Alessandro Sarti

Abstract: Confocal laser scanning microscopy provides nondestructive in vivo imaging to capture specific structures that have been fluorescently labeled, such as cell nuclei and membranes, throughout early Zebrafish embryogenesis. With this strategy we aim at reconstruct in time and space the biological structures of the embryo during the organogenesis. In this paper we propose a method to extract bounding surfaces at the cellular-organization level from microscopy images. The shape reconstruction of membranes and nuclei is obtained first with an automatic identification of the cell center and then a subjective surfaces based segmentation is used to extract the bounding surfaces.
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Paper Nr: 364
Title:

INCORPORATING A NEW RELATIONAL FEATURE IN ARABIC ONLINE HANDWRITTEN CHARACTER RECOGNITION

Authors:

Sara Izadi and Ching Y. Suen

Abstract: Artificial neural networks have shown good performance in classification tasks. However, models used for learning in pattern classification are challenged when the differences between the patterns of the training set are small. Therefore, the choice of effective features is mandatory for obtaining good performance. Statistical and geometrical features alone are not suitable for recognition of hand printed characters due to variations in writing styles that may result in deformations of character shapes. We address this problem by using a relational context feature combined with a local descriptor for training a neural network-based recognition system in a user-independent online character recognition application. Our feature extraction approach provides a rich representation of the global shape characteristics, in a considerably compact form. This new relational feature provides a higher distinctiveness and increases robustness with respect to character deformations. While enhancing the recognition accuracy, the feature extraction is computationally simple. We show that the ability to discriminate in Arabic handwriting characters is increased by adopting this mechanism in feed forward neural network architecture. Our experiments on Arabic character recognition show comparable results with the state-of-the-art methods for online recognition of these characters.
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Area 3 - Image Understanding

Full Papers
Paper Nr: 42
Title:

HARMONIC DEFORMATION MODEL FOR EDGE BASED TEMPLATE MATCHING

Authors:

Andreas Hofhauser, Carsten Steger and Nassir Navab

Abstract: The paper presents an approach to the detection of deformable objects in single images. To this end we propose a robust match metric that preserves the relative edge point neighborhood, but allows significant shape changes. Similar metrics have been used for the detection of rigid objects (Olson and Huttenlocher, 1997; Steger, 2002). To the best of our knowledge this adaptation to deformable objects is new. In addition, we present a fast algorithm for model deformation. In contrast to the widely used thin-plate spline (Bookstein, 1989; Donato and Belongie, 2002), it is efficient even for several thousand points. For arbitrary deformations, a forward-backward interpolation scheme is utilized. It is based on harmonic inpainting, i.e. it regularizes the displacement in order to obtain smooth deformations. Similar to optical flow, we obtain a dense deformation field, though the template contains only a sparse set of model points. Using a coarse-to-fine representation for the distortion of the template further increases efficiency. We show in a number of experiments that the presented approach in not only fast, but also very robust in detecting deformable objects.
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Paper Nr: 50
Title:

NEW INVARIANT DESCRIPTORS BASED ON THE MELLIN TRANSFORM

Authors:

Samy Metari and François Deschênes

Abstract: In this paper we introduce two new classes of radiometric and combined radiometric-geometric invariant descriptors. The first class includes two types of radiometric descriptors. The first type is based on the Mellin transform and the second one is based on central moments. Both descriptors are invariant to contrast changes and to convolution with any kernel having a symmetric form with respect to the diagonals. The second class contains two subclasses of combined descriptors. The first subclass includes central-moment based descriptors invariant simultaneously to translations, to uniform and anisotropic scaling, to stretching, to contrast changes and to convolution. The second subclass includes central-complex-moment based descriptors invariant simultaneously to similarity transformation and to contrast changes. We apply those invariants to the matching of geometrically transformed and/or blurred images.
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Paper Nr: 65
Title:

ROBUST FACE ALIGNMENT USING CONVOLUTIONAL NEURAL NETWORKS

Authors:

Stefan Duffner and Christophe Garcia

Abstract: Face recognition in real-world images mostly relies on three successive steps: face detection, alignment and identification. The second step of face alignment is crucial as the bounding boxes produced by robust face detection algorithms are still too imprecise for most face recognition techniques, i.e. they show slight variations in position, orientation and scale. We present a novel technique based on a specific neural architecture which, without localizing any facial feature points, precisely aligns face images extracted from bounding boxes coming from a face detector. The neural network processes face images cropped using misaligned bounding boxes and is trained to simultaneously produce several geometric parameters characterizing the global misalignment. After having been trained, the neural network is able to robustly and precisely correct translations of up to ±13% of the bounding box width, in-plane rotations of up to ±30◦ and variations in scale from 90% to 110%. Experimental results show that 94% of the face images of the BioID database and 80% of the images of a complex test set extracted from the internet are aligned with an error of less than 10% of the face bounding box width.
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Paper Nr: 66
Title:

INVARIANT FACE RECOGNITION IN A NETWORK OF CORTICAL COLUMNS

Authors:

Philipp Wolfrum, Jörg Lücke and Christoph von der Malsburg

Abstract: We describe a neural network for invariant object recognition. The network is generative in the sense that it explicitly represents both the recognized object and the extrinsic properties to which it is invariant (especially object position). The model is biologically plausible, being formulated as a neuronal system composed of cortical columns. At the same time it has competitive face recognition performance.
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Paper Nr: 125
Title:

IMAGE COMPLETION USING A DIFFUSION DRIVEN MEAN CURVATURE FLOW IN A SUB-RIEMANNIAN SPACE

Authors:

Gonzalo Sanguinetti, Giovanna Citti and Giovanna Citti

Abstract: In this paper we present an implementation of a perceptual completion model performed in the three dimensional space of position and orientation of level lines of an image. We show that the space is equipped with a natural subriemannian metric. This model allows to perform disocclusion representing both the occluding and occluded objects simultaneously in the space. The completion is accomplished by computing minimal surfaces with respect to the non Euclidean metric of the space. The minimality is achieved via diffusion driven mean curvature flow. Results are presented in a number of cognitive relevant cases.
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Paper Nr: 142
Title:

DRIVING WARNING SYSTEM BASED ON VISUAL PERCEPTION OF ROAD SIGNS

Authors:

Juan Pablo Carrasco, Arturo De La Escalera and Jose Maria Armingol

Abstract: Advanced Driver Assistance Systems are used to increase the security of vehicles. Computer Vision is one of the main technologies used for this aim. Lane marks recognition, pedestrian detection, driver drowsiness or road sign detection and recognition are examples of these systems. The last one is the goal of this paper. A system that can detect and recognize road signs based on color and shape features is presented in this article. It will be focused on detection, especially the color space used, investigating on the case of road signs under shadows. The system, also tracks the road sign once it has been detected. It warns the driver in case of anomalous speed for the recognized road sign using the information from a GPS.
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Paper Nr: 168
Title:

ON THE CONTRIBUTION OF COMPRESSION TO VISUAL PATTERN RECOGNITION

Authors:

Gunther Heidemann and Helge Ritter

Abstract: Most pattern recognition problems are solved by highly task specific algorithms. However, all recognition and classification architectures are related in at least one aspect: They rely on compressed representations of the input. It is therefore an interesting question how much compression itself contributes to the pattern recognition process. The question has been answered by Benedetto et al. (2002) for the domain of text, where a common compression program (gzip ) is capable of language recognition and authorship attribution. The underlying principle is estimating the mutual information from the obtained compression factor. Here we show that compression achieves astonishingly high recognition rates even for far more complex tasks: Visual object recognition, texture classification, and image retrieval. Though, naturally, specialized recognition algorithms still outperform compressors, our results are remarkable, since none of the applied compression programs (gzip , bzip2 ) was ever designed to solve this type of tasks. Compression is the only known method that solves such a wide variety of tasks without any modification, data preprocessing, feature extraction, even without parametrization. We conclude that compression can be seen as the “core” of a yet to develop theory of unified pattern recognition.
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Paper Nr: 190
Title:

SUBJECT RECOGNITION USING A NEW APPROACH FOR FEATURE SELECTION

Authors:

Agata Lapedriza Garcia, David Masip Rodo and Jordi Vitria Marca

Abstract: In this paper we propose a feature selection method that uses the mutual information (MI) measure on a Principal Component Analysis (PCA) based decomposition. PCA finds a linear projection of the data in a non-supervised way, which preserves the larger variance components of the data under the reconstruction error criterion. Previous works suggest that using the MI among the PCA projected data and the class labels applied to feature selection can add the missing discriminability criterion to the optimal reconstruction feature set. Our proposal goes one step further, defining a global framework to add independent selection criteria in order to filter misleading PCA components while the optimal variables for classification are preserved. We apply this approach to a face recognition problem using the AR Face data set. Notice that, in this problem, PCA projection vectors strongly related to illumination changes and occlusions are usually preserved given their high variance. Our additional selection tasks are able to discard this type of features while the relevant features to perform the subject recognition classification are kept. The experiments performed show an improved feature selection process using our combined criterion.
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Paper Nr: 372
Title:

FACE MODEL FITTING WITH GENERIC, GROUP-SPECIFIC, AND PERSON-SPECIFIC OBJECTIVE FUNCTIONS

Authors:

Sylvia Pietzsch, Matthias Wimmer, Freek Stulp and Bernd Radig

Abstract: In model-based fitting, the model parameters that best fit the image are determined by searching for the optimum of an objective function. Often, this function is designed manually, based on implicit and domain-dependent knowledge. We acquire more robust objective function by learning them from annotated images, in which many critical decisions are automated, and the remaining manual steps do not require domain knowledge. Still, the trade-off between generality and accuracy remains. General functions can be applied to a large range of objects, whereas specific functions describe a subset of objects more accurately. (Gross et al., 2005) have demonstrated this principle by comparing generic to person-specific Active Appearance Models. As it is impossible to learn a person-specific objective function for the entire human population, we automatically partition the training images and then learn partition-specific functions. The number of groups influences the specificity of the learned functions. We automatically determine the optimal partitioning given the number of groups, by minimizing the expected fitting error. Our empirical evaluation demonstrates that the group-specific objective functions more accurately describe the images of the corresponding group. The results of this paper are especially relevant to face model tracking, as individual faces will not change throughout an image sequence.
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Paper Nr: 376
Title:

HIERARCHICAL EVALUATION MODEL FOR 3D FACE RECOGNITION

Authors:

Sídnei A. Drovetto Jr., Luciano Silva and Olga Regina Pereira Bellon

Abstract: In this paper we propose to perform 3D face matching based on alignments obtained using Simulated Annealing (SA) algorithm guided by the Mean Squared Error (MSE) with M-estimator Sample Consensus (MSAC) and the Surface Interpenetration Measure (SIM). The matching score is obtained by calculation of the SIM after the registration process. Since the SIM is a sensitive measure, it needs a good alignment to give relevance to its value. Our registration approach tends to reach a near global solution and, therefore, produces the necessary precise alignments. By analyzing the matching score, the system can identify if the input images come from the same subject or not. In a verification scenario, we use a hierarchical evaluation model which maximizes the results and reduces the computing time. Extensive experiments were performed on the well-known Face Recognition Grand Challenge (FRGC) v2.0 3D face database using five different facial regions: three regions of the nose; the region of the eyes; and the face itself. Compared to state-of-the-art works, our approach has achieved a high rank-one recognition rate and a high verification rate.
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Paper Nr: 379
Title:

SINGLE-IMAGE 3D RECONSTRUCTION OF BALL VELOCITY AND SPIN FROM MOTION BLUR - An Experiment in Motion-from-Blur

Authors:

Giacomo Boracchi, Vincenzo Caglioti and Alessandro Giusti

Abstract: We present an algorithm for analyzing a single calibrated image of a ball and for reconstructing its instantaneous motion (3D velocity and spin) by exploiting motion blur. We use several state-of-the-art image processing techniques for extracting information from the space-variant blurred image, then robustly integrate such information in a geometrical model of the 3D motion. We initially handle the simpler case in which the ball apparent translation is neglegible w.r.t. its spin, then extend the technique to handle the most general motion. We show extensive experimental results both on synthetic and camera images. In a broader scenario, we exploit this specific problem for discussing motivations, advantages and limits of reconstructing motion from motion blur.
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Short Papers
Paper Nr: 12
Title:

A CORRECTIVE FRAMEWORK FOR FACIAL FEATURE DETECTION AND TRACKING

Authors:

Hussein Hamshari, Steven Beauchemin, Denis Laurendeau and Normand Teasdale

Abstract: Epidemiological studies indicate that automobile drivers from varying demographics are confronted by difficult driving contexts such as negotiating intersections, yielding, merging and overtaking. We aim to detect and track the face and eyes of the driver during several driving scenarios, allowing for further processing of a driver’s visual search pattern behavior. Traditionally, detection and tracking of objects in visual media has been performed using specific techniques. These techniques vary in terms of their robustness and computational cost. This research proposes a framework that is built upon a foundation synonymous to boosting. The idea of an integrated framework employing multiple trackers is advantageous in forming a globally strong tracking methodology. In order to model the effectiveness of trackers, a confidence parameter is introduced to help minimize the errors produced by incorrect matches and allow more effective trackers with a higher confidence value to correct the perceived position of the target.
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Paper Nr: 40
Title:

LOSS-WEIGHTED DECODING FOR ERROR-CORRECTING OUTPUT CODIN

Authors:

Sergio Escalera, Oriol Pujol and Petia Radeva

Abstract: The multi-class classification is a challenging problem for several applications in Computer Vision. Error Correcting Output Codes technique (ECOC) represents a general framework capable to extend any binary classification process to the multi-class case. In this work, we present a novel decoding strategy that takes advantage of the ECOC coding to outperform the up to now existing decoding strategies. The novel decoding strategy is applied to the state-of-the-art coding designs, extensively tested on the UCI Machine Learning repository database and in two real vision applications: tissue characterization in medical images and traffic sign categorization. The results show that the presented methodology considerably increases the performance of the traditional ECOC strategies and the state-of-the-art multi-classifiers.
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Paper Nr: 46
Title:

A NEW FACE RECOGNITION SYSTEM - Using HMMs Along with SVD Coefficients

Authors:

Pooya Davari and Hossein Miar Naimi

Abstract: In this paper, a new Hidden Markov Model (HMM)-based face recognition system is proposed. As a novel point despite of 5-state HMM used in pervious researches, we used 7-state HMM to cover more details. As another novel point, we used a small number of quantized Singular Value Decomposition (SVD) coefficients as features describing blocks of face images. This makes the system very fast. In order to additional reduction in computational complexity and memory consumption the images are resized to 64 × 64 jpeg format. The system has been examined on the Olivetti Research Laboratory (ORL) face database. The experiments showed a recognition rate of 99%, using half of the images for training. Our system has been evaluated on YALE database too. Using five and six training images, we obtained 97.78% and 100% recognition rates respectively, a record in the literature. The proposed method is compared with the best researches in the literature. The results show that the proposed method is the fastest one, having approximately 100% recognition rate.
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Paper Nr: 60
Title:

EYE DETECTION USING LINE EDGE MAP TEMPLATE

Authors:

Mihir Jain, Suman K. Mitra and Naresh D. Jotwani

Abstract: Location of eyes is an important visual clue for processes such as scaling and orientation correction, which are precursors to face recognition. This paper presents a robust algorithm for eye detection which makes use of edge information and distinctive features of eyes, starting from a roughly localized face image. Potential region pairs are generated, and then template matching is applied to match these region pairs with a generated eye line edge map template using primary line segment Hausdorff distance to get an estimation of the centers of two eyes. This result is then refined to get iris centers and also eye centers. Experimental results demonstrate the excellent performance of the proposed algorithm.
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Paper Nr: 63
Title:

VIDEO EVENT CLASSIFICATION AND DETECTION USING 2D TRAJECTORIES

Authors:

Alexandre Hervieu, Patrick BOUTHEMY and Jean-Pierre Le Cadre

Abstract: This paper describes an original statistical trajectory-based approach which can address several issues related to dynamic video content understanding: unsupervised clustering of events, recognition of events corresponding to learnt classes of dynamic video contents, and detection of unexpected events. Appropriate local differ- ential features combining curvature and motion magnitude are robustly computed on the trajectories. They are invariant to image translation, in-the-plane rotation and scale transformation. The temporal causality of these features is then captured by hidden Markov models whose states are properly quantized values, and similarity between trajectories is expressed by exploiting the HMM framework. We report experiments on two sets of data, a first one composed of typical classes of synthetic (noised) trajectories (such as parabola or clothoid), and a second one formed with trajectories computed in sports videos. We have also favorably compared our method to other ones, including feature histogram comparison, use of the longest common subsequence (LCSS) distance and SVM-based classification.
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Paper Nr: 64
Title:

FACE AND FACIAL FEATURE DETECTION EVALUATION - Performance Evaluation of Public Domain Haar Detectors for Face and Facial Feature Detection

Authors:

Modesto Castrillon, O. Déniz-Suárez, L. Antón-Canalís and J. Lorenzo-Navarro

Abstract: Fast and reliable face and facial feature detection are required abilities for any Human Computer Interaction approach based on Computer Vision. Since the publication of the Viola-Jones object detection framework and the more recent open source implementation, an increasing number of applications have appeared, particularly in the context of facial processing. In this respect, the OpenCV community shares a collection of public domain classifiers for this scenario. However, as far as we know these classifiers have never been evaluated and/or compared. In this paper we analyze the individual performance of all those public classifiers getting the best performance for each target. These results are valid to define a baseline for future approaches. Additionally we propose a simple hierarchical combination of those classifiers to increase the facial feature detection rate while reducing the face false detection rate.
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Paper Nr: 122
Title:

TEXTURE BASED DESCRIPTION OF MOVEMENTS FOR ACTIVITY ANALYSIS

Authors:

Vili Kellokumpu, Guoying Zhao and Matti Pietikäinen

Abstract: Human motion can be seen as a type of moving texture pattern. In this paper, we propose a novel approach for activity analysis by describing human activities with texture features. Our approach extracts spatially enhanced local binary pattern (LBP) histograms from temporal templates (Motion History Images and Motion Energy Images) and models their temporal behavior with hidden Markov models. The description is useful for action modeling and is suitable for detecting and recognizing various kinds of activities. The method is computationally simple. We perform tests on two published databases and clearly show the good performance of our approach in classification and detection tasks. Furthermore, experimental results show that the approach performs robustly against irregularities in data, such as limping and walking with a dog, partial occlusions and low video quality.
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Paper Nr: 150
Title:

A MULTI-SCALE LAYOUT DESCRIPTOR BASED ON DELAUNAY TRIANGULATION FOR IMAGE RETRIEVAL

Authors:

Agnés Borràs Angosto and Josep Lladós Canet

Abstract: Working with large collections of videos and images has need of effective and flexible techniques of retrieval and browsing. Beyond the classical color histogram approaches, the layout information has proven to be a very descriptive cue for image description. We have developed a descriptor that encodes the layout of an image using a histogram-based representation. The descriptor uses a multi-layer representation that captures the saliency of the image parts. Furthermore it encodes their relative positions using the properties of a Delaunay triangulation. The descriptor is a compact feature vector which content is normalized. Their properties make it suitable for image retrieval and indexing applications. Finally, have applied it to a video browsing application that detects characteristic scenes of a news program.
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Paper Nr: 151
Title:

A SIGNAL-SYMBOL LOOP MECHANISM FOR ENHANCED EDGE EXTRACTION

Authors:

Sinan Kalkan, Florentin Woergoetter, Shi Yan, Volker Krueger and Norbert Krueger

Abstract: The transition to symbolic information from images involves in general the loss or misclassification of information. One way to deal with this missing or wrong information is to get feedback from concrete hypotheses derived at a symbolic level to the sub-symbolic (signal) stage to amplify weak information or correct misclassifications. This paper proposes such a feedback mechanism between the symbolic level and the signal level, which we call signal symbol loop. We apply this framework for the detection of low contrast edges making use of predictions based on Rigid Body Motion. Once the Rigid Body Motion is known, the location and the properties of edges at a later frame can be predicted. We use these predictions as feedback to the signal level at a later frame to improve the detection of low contrast edges. We demonstrate our mechanism on a real example, and evaluate the results using an artificial scene, where the ground truth data is available.
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Paper Nr: 197
Title:

EFFICIENT OBJECT DETECTION USING PCA MODELING AND REDUCED SET SVDD

Authors:

Rudra N. Hota and Venkataramana K. B.

Abstract: Object detection problem is traditionally tackled as two class problem. Wherein the non object classes are not precisely defined. In this paper we propose cascade of principal component modeling with associated test statistics and reduced set support vector data description for efficient object detection, both of which hinge mainly on modeling of object class training data. The PCA modeling enables quick rejection of comparatively obvious non object in initial stage of the cascade to gain computation advantage. The reduced set SVDD is applied in latter stages of cascade to classify relatively difficult images. This combination of PCA modeling and reduced set support vector data description leads to a good object detection with simple pixel features.
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Paper Nr: 211
Title:

FAST TEMPLATE MATCHING FOR MEASURING VISIT FREQUENCIES OF DYNAMIC WEB ADVERTISEMENTS

Authors:

Dániel Szolgay, Csaba Benedek and Tamás Szirányi

Abstract: In this paper an on-line method is proposed for statistical evaluation of dynamic web advertisements via measuring their visit frequencies. To minimize the required user-interaction, the eye movements are tracked by a special eye camera, and the hits on advertisements are automatically recognized. The detection step is mapped to a 2D template matching problem, and novel algorithms are developed to significantly decrease the processing time, via excluding quickly most of the false hit-candidates. We show that due to the improvements the method runs in real time in the context of the selected application. The solution has been validated on real test data and quantitave results have been provided to show the gain in recognition rate and processing time versus previous approaches.
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Paper Nr: 227
Title:

OBJECTIVE EVALUATION OF SEAM PUCKER USING AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

Authors:

K. L. Mak and WEI LI

Abstract: Seam pucker evaluation plays a very important role in the garments manufacturing industry. At present, seam puckers are usually evaluated by human inspectors, which is subjective, unreliable and time-consuming. With the developments of image processing and pattern recognition technologies, an automatic vision-based seam pucker evaluation system becomes possible. This paper presents a new approach based on adaptive neuro-fuzzy inference system (ANFIS) to establish the relationship between seam pucker grades and textural features of seam pucker images. The evaluation procedure is performed in two stages: features extraction with the co-occurrence matrix approach, and classification with ANFIS. Experimental results demonstrate the validity and effectiveness of the proposed ANFIS-based method.
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Paper Nr: 254
Title:

IMAGE ANNOTATION WITH RELEVANCE FEEDBACK USING A SEMI-SUPERVISED AND HIERARCHICAL APPROACH

Authors:

Cheng-Chieh Chiang, Yi-Ping Hung, Ming-Wei Hung and Wee Kheng Leow

Abstract: This paper presents an approach for image annotation with relevance feedback that interactively employs a semi-supervised learning to build hierarchical classifiers associated with annotation labels. We construct individual hierarchical classifiers each corresponding to one semantic label that is used for describing the semantic contents of the images. We adopt hierarchical approach for classifiers to divide the whole semantic concept associated with a label into several parts such that the complex contents in images can be simplified. We also design a semi-supervised approach for learning classifiers reduces the need of training images by use of both labeled and unlabeled images. This proposed semi-supervised and hierarchical approach is involved in an interactive scheme of relevance feedbacks to assist the user in annotating images. Finally, we describe some experiments to show the performance of the proposed approach.
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Paper Nr: 266
Title:

TOWARDS EMBEDDED WASTE SORTING - Using Constellations of Visual Words

Authors:

Toon Goedemé

Abstract: In this paper, we present a method for fast and robust object recognition, especially developed for implementation on an embedded platform. As an example, the method is applied to the automatic sorting of consumer waste. Out of a stream of different thrown-away food packages, specific items — in this case beverage cartons — can be visually recognised and sorted out. To facilitate and optimise the implementation of this algorithm on an embedded platform containing parallel hardware, we developed a voting scheme for constellations of visual words, i.e. clustered local features (SURF in this case). On top of easy implementation and robust and fast performance, even with large databases, an extra advantage is that this method can handle multiple identical visual features in one model.
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Paper Nr: 273
Title:

LOW-LEVEL FUSION OF AUDIO AND VIDEO FEATURE FOR MULTI-MODAL EMOTION RECOGNITION

Authors:

Matthias Wimmer, Björn Schuller, Dejan Arsic, Gerhard Rigoll and Bernd Radig

Abstract: Bimodal emotion recognition through audiovisual feature fusion has been shown superior over each individual modality in the past. Still, synchronization of the two streams is a challenge, as many vision approaches work on a frame basis opposing audio turn- or chunk-basis. Therefore, late fusion schemes such as simple logic or voting strategies are commonly used for the overall estimation of underlying affect. However, early fusion is known to be more effective in many other multimodal recognition tasks. We therefore suggest a combined analysis by descriptive statistics of audio and video Low-Level-Descriptors for subsequent static SVM Classification. This strategy also allows for a combined feature-space optimization which will be discussed herein. The high effectiveness of this approach is shown on a database of 11.5h containing six emotional situations in an airplane scenario.
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Paper Nr: 291
Title:

EFFICIENT OBJECT DETECTION ROBUST TO RST WITH MINIMAL SET OF EXAMPLES

Authors:

Sebastien Onis, Henri Sanson, Christophe Garcia and Jean-Luc Dugelay

Abstract: In this paper, we present an object detection approach based on a similarity measure combining cross-correlation and affine deformation. Current object detection systems provide good results, at the expense of requiring a large training database. The use of correlation anables object detection with very small training set but is not robust to the luminosity change and RST (Rotation, Scale, translation) transformation. This paper presents a detection system that first searches the likely positions and scales of the object using image preprocessing and cross-correlation method and secondly, uses a similarity measure based on affine deformation to confirm or not the predetection. We apply our system to face detection and show the improvement in results due to the images preprocessing and the affine deformation.
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Paper Nr: 294
Title:

RELATIONS BETWEEN RECONSTRUCTED 3D ENTITIES

Authors:

Nicolas Pugeault, Sinan Kalkan, Florentin Woergoetter, Emre Baseski and Norbert Kruger

Abstract: In this paper, we first propose an analytic formulation for the position’s and orientation’s uncertainty of local 3D line descriptors reconstructed by stereo. We evaluate these predicted uncertainties with Monte Carlo simulations, and study their dependency on different parameters (position and orientation). In a second part, we use this definition to derive a new formulation for inter–features distance and coplanarity. These new formulations take into account the predicted uncertainty, allowing for better robustness. We demonstrate the positive effect of the modified definitions on some simple scenarios.
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Paper Nr: 354
Title:

RECOGNITION OF TEXT WITH KNOWN GEOMETRIC AND GRAMMATICAL STRUCTURE

Authors:

Jan Rathousky, Martin Urban and Vojtech Franc

Abstract: The optical character recognition (OCR) module is a fundamental part of each automated text processing system. The OCR module translates an input image with a text line into a string of symbols. In many applications (e.g. license plate recognition) the text has some a priori known geometric and grammatical structure. This article proposes an OCR method exploiting this knowledge which restricts the set of possible strings to a limited set of feasible combinations. The recognition task is formulated as maximization of a similarity function which uses character templates as reference. These templates are estimated by a support vector machine method from a set of examples. In contrast to the common approach, the proposed method performs character segmentation and recognition simultaneously. The method was successfully evaluated in a car license plate recognition system.
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Paper Nr: 358
Title:

REPRESENTATION AND RECOGNITION OF HUMAN ACTIONS - A New Approach based on an Optimal Control Motor Model

Authors:

Sumitra Ganesh and Ruzena Bajcsy

Abstract: We present a novel approach to the problem of representation and recognition of human actions, that uses an optimal control based model to connect the high-level goals of a human subject to the low-level movement trajectories captured by a computer vision system. These models quantify the high-level goals as a performance criterion or cost function which the human sensorimotor system optimizes by picking the control strategy that achieves the best possible performance. We show that the human body can be modeled as a hybrid linear system that can operate in one of several possible modes, where each mode corresponds to a particular high-level goal or cost function. The problem of action recognition, then is to infer the current mode of the system from observations of the movement trajectory. We demonstrate our approach on 3D visual data of human arm motion.
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Paper Nr: 373
Title:

SIMILARITY MEASURES FUSION USING SVM CLASSIFIER FOR FACE AUTHENTICATION

Authors:

Mohammad T. Sadeghi, Masoumeh Samiei, Seyed Mohammad T Almodarresi and Josef Kittler

Abstract: In this paper, the problems of measuring similarity in LDA face space using different metrics and fusing the associated classifiers are considered. A few similarity measures used in different pattern recognition applications, including the recently proposed Gradient Direction (GD) metric are reviewed. An automatic parameter selection algorithm is then proposed for optimising the GD metric. In extensive experimentation on the BANCA database, we show that the optimised GD metric outperforms the other metrics in various conditions. Moreover, we demonstrate that by combining the GD metric and seven other metrics in the decision level using Support Vector Machines, the performance of the resulting decision making scheme consistently improves.
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Paper Nr: 382
Title:

COMPLETE AND STABLE PROJECTIVE HARMONIC INVARIANTS FOR PLANAR CONTOURS RECOGNITION

Authors:

Faten Chaieb and Faouzi Ghorbel

Abstract: Planar shapes recognition is an important problem in computer vision and pattern recognition. We deal with planar shape contour views that differ by a general projective transformation. One method for solving such problem is to use projective invariants. In this work, we propose a projective and parameterization invariant generation framework based on the harmonic analysis theory. In fact, invariance to reparameterization is obtained by a projective arc length curve reparameterization process. Then, a complete and stable set of projective harmonic invariants is constructed from the Fourier coefficients computed on the reparameterized contours. We experiment this set of descriptors on analytic contours in order to recognize projectively similar ones.
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Paper Nr: 403
Title:

FACIAL EXPRESSION RECOGNITION USING ACTIVE APPEARANCE MODELS

Authors:

Pedro Martins, Joana Sampaio and Jorge Batista

Abstract: A framework for automatic facial expression recognition combining Active Appearance Model (AAM) and Linear Discriminant Analysis (LDA) is proposed. Seven different expressions of several subjects, representing the neutral face and the facial emotions of happiness, sadness, surprise, anger, fear and disgust were analysed. The proposed solution starts by describing the human face by an AAM model, projecting the appearance results to a Fisherspace using LDA to emphasize the different expression categories. Finaly the performed classification is based on malahanobis distance.
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Paper Nr: 32
Title:

DIFFUSION FILTERING FOR ILLUMINATION INVARIANT FACE RECOGNITION - Illumination Approximation with Diffusion Filters within Retinex Context

Authors:

Peter Dunker and Melanie Keller

Abstract: Face recognition becomes a very important technology in recent years for a lot of various applications. One major problem of the most state-of-the-art algorithms are different lightning conditions which can decrease recognition rates dramatically. To reduce the influence of illumination in the recognition process normalization methods can be used. In this paper we introduce illumination normalization algorithms based on diffusion filters. Further we compare our approaches with selected established algorithms. Finally we present our evaluation results based on well known face recognitions techniques and an appropriate face database. The results show that the diffusion filter approaches outperforms all other algorithms which demonstrates the capabilities of the diffusion filter technology for illumination normalization in face recognition.
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Paper Nr: 83
Title:

IMAGE RETRIEVAL USING KRAWTCHOUK CHROMATICITY DISTRIBUTION MOMENTS

Authors:

Evangelia Tziola, Konstantinos Konstantinidis, Leonidas Kotoulas and Ioannis Andreadis

Abstract: In this paper a set of Krawtchouk Chromaticity Distribution Moments (KCDMs) for the effective representation of image color content is introduced. The proposed method describes chromaticity through a set of KCDMs applied on the associated chromaticity distribution function in the L*a*b* color space. The computational requirements of this approach are relatively small, compared to other methods addressing the issue of image retrieval using color features. This has a direct impact on the time required to index an image database. Furthermore, due to the short-length of KCDMs feature vector, there is a straight reduction on the time needed to retrieve the whole database. Comparing to previous relative works, KCDMs provide a more accurate representation of the L*a*b* chromaticity distribution functions, since no numerical approximation is involved in deriving the moments. Furthermore, unlike other orthogonal moments, Krawtchouk moments can be employed to extract local features of a chromaticity diagram. This property makes them more analytical near the centre of mass of the chromaticity distribution. The theoretical framework is validated by experiments which prove the superior performance of KCDMs above other methods.
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Paper Nr: 113
Title:

AUTOMATED OBJECT SHAPE MODELLING BY CLUSTERING OF WEB IMAGES

Authors:

Giuseppe Scardino, Ignazio Infantino and Salvatore Gaglio

Abstract: The paper deals with the description of a framework to create shape models of an object using images from the web. Results obtained from different image search engines using simple keywords are filtered, and it is possible to select images viewing a single object owning a well-defined contour. In order to have a large set of valid images, the implemented system uses lexical web databases (e.g. WordNet) or free web encyclopedias (e.g. Wikipedia), to get more keywords correlated to the given object. The shapes extracted from selected images are represented by Fourier descriptors, and are grouped by K-means algorithm. Finally, the more representative shapes of main clusters are considered as prototypical contours of the object. Preliminary experimental results are illustrated to show the effectiveness of the proposed approach.
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Paper Nr: 120
Title:

SPATIAL NEIGHBORING HISTOGRAM FOR SHAPE-BASED IMAGE RETRIEVAL

Authors:

Noramiza Hashim, Patrice Boursier and Hong Tat Ewe

Abstract: Man-made object recognition from ground level image requires a fast and efficient approach especially in a large image database. Our work focuses on recognizing buildings based on a shape-based histogram descriptor. A 2-dimensional histogram is generated from gradient direction information of edge pixels and local spatial analysis of its neighbors. The edge direction histogram is a global representation of edge pixels. The neighborhood structure is coded in a 4-bit binary representation which offers a simple and efficient way to incorporate local spatial data into the histogram. We find that the proposed spatial neighboring histogram increases the retrieval precision by approximately 10% compared to other shape-based histogram methods.
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Paper Nr: 127
Title:

AN AUTOMATIC WELDING DEFECTS CLASSIFIER SYSTEM

Authors:

Juan Zapata, Ramón Ruiz and Rafael Vilar

Abstract: Radiographic inspection is a well-established testing method to detect weld defects. However, interpretation of radiographic films is a difficult task. The reliability of such interpretation and the expense of training suitable experts have allowed that the efforts being made towards automation in this field. In this paper, we describe an automatic detection system to recognise welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling, were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of geometrical features which characterise the defect shape and orientation was proposed and extracted between defect candidates. In a third stage, an artificial neural network for weld defect classification was used under three regularisation process with different architectures. For the input layer, the principal component analysis technique was used in order to reduce the number of feature variables; and, for the hidden layer, a different number of neurons was used in the aim to give better performance for defect classification in both cases. The proposed classification consists in detecting the four main types of weld defects met in practice plus the non-defect type.
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Paper Nr: 129
Title:

INVARIANT CODES FOR SIMILAR TRANSFORMATION AND ITS APPLICATION TO SHAPE MATCHING

Authors:

eiji yoshida and seiichi mita

Abstract: In this paper, we propose a new method for the measurement of shape similarity. Our proposed method encodes the contour of an object by using the curvature of the object. If one objects are similar (under translation, rotation, and scaling) in shape to the other, these codes themselves or their cyclic shift have the same values. We compare our method with other methods such as CSS (curvature scale space), and shape context. We show that the recognition rate of our method is 100 % and 90.40 % for the rotation and scaling robustness test using MPEG7-CE-Shape1 and 81.82 % and 95.14 % for the similarity-based retrieval test and the occlusion test using Kimia's silhouette. In particular, the value of the occlusion test is approximately 25 % higher than those of CSS, SC. Moreover, we show that the computational cost of our method is not so large by comparison our method with above methods.
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Paper Nr: 147
Title:

FAST WIREFRAME-VISIBILITY ALGORITHM

Authors:

Ezgi Kiper

Abstract: In this paper, a fast wireframe-visibility algorithm is introduced. The algorithm’s inputs are 3D wireframe model of an object, internal and external camera calibration parameters. Afterwards, the algorithm outputs the 2D image of the object with only visible lines and surfaces. 2D image of an object is constructed by using a camera model with the given camera calibration parameters and 3D wireframe object model. The idea behind the algorithm is finding the intersection points of all lines in 2D image of the object. These intersection points are called as critical points and the lines having them are critical lines. Lines without any critical points are regarded as normal lines. Critical and normal lines are processed separately. Critical lines are separated into smaller lines by its critical points and depth calculation is performed for the middle points of these smaller lines. For the normal lines, depth of the middle point of the normal line is calculated to determine if it is visible or not. As a result, the algorithm provides the minimum amount of point’s depth calculation. Moreover, this idea provides much faster process for the reason that there aren’t any resolution and memory problems like well-known image-space scan-line and z-buffering algorithms.
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Paper Nr: 161
Title:

CLASSIFIER SELECTION FOR FACE RECOGNITION ALGORITHM BASED ON ACTIVE SHAPE MODEL

Authors:

Andrzej Florek and MACIEJ KRÓL

Abstract: In this paper, experimental results from the face contour classification tests are shown. The presented approach is dedicated to a face recognition algorithm based on the Active Shape Model. The results were obtained from experiments carried out on the set of 2700 images taken from 100 persons. Manually fitted contours (194 samples for eight components of one face contour) were classified after feature space decomposition carried out by the Linear Discriminant Analysis or by the Support Vector Machines algorithms.
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Paper Nr: 172
Title:

HIGH PERFORMANCE POSE INVARIANT FACE RECOGNITION

Authors:

Hasan Demirel and Gholamreza Anbarjafari

Abstract: A novel pose invariant face recognition system based on grey level histogram matching is proposed. The proposed system in this paper uses grey level histograms as feature vectors for recognition of the different poses of faces. The process is performed by taking the cross correlation between the histogram of a test face and the histograms of the training faces in the database. The proposed system gives 98.80% recognition rate on the HP database of 15 face subjects. This rate is down to 92% in the case of conventional eigenfaces method.
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Paper Nr: 201
Title:

RELEVANCE FEEDBACK WITH MAX-MIN POSTERIOR PSEUDO-PROBABILITY FOR IMAGE RETRIEVAL

Authors:

Yuan Deng, Xiabi Liu and Yunde Jia

Abstract: This paper proposes a new relevance feedback method for image retrieval based on max-min posterior pseudo-probabilities (MMP) framework. We assume that the feature vectors extracted from the relevant images be of the distribution of Gaussian mixture model (GMM). The corresponding posterior pseudo-probability function is used to classify images into two categories: relevant to the user intention and irrelevant. The images relevant to the user intention are returned as the retrieval results which are then labelled as true of false by the user. We further apply MMP training criterion to update the parameter set of the posterior pseudo-probability function from the labelled retrieval results. Subsequently, new retrieval results are returned. Our method of relevance feedback was tested on Corel database and the experimental results show the effectiveness of the proposed method.
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Paper Nr: 210
Title:

TEXT DETECTION WITH CONVOLUTIONAL NEURAL NETWORKS

Authors:

Manolis Delakis and Christophe Garcia

Abstract: Text detection is an important preliminary step before text can be recognized in unconstrained image environments. We present an approach based on convolutional neural networks to detect and localize horizontal text lines from raw color pixels. The network learns to extract and combine its own set of features through learning instead of using hand-crafted ones. Learning was also used in order to precisely localize the text lines by simply training the network to reject badly-cut text and without any use of tedious knowledge-based post-processing. Although the network was trained with synthetic examples, experimental results demonstrated that it can outperform other methods on the real-world test set of ICDAR’03.
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Paper Nr: 249
Title:

A BAYESIAN APPROACH TO 3D OBJECT RECOGNITION USING LINEAR COMBINATION OF 2D VIEWS

Authors:

Vasileios Zografos and Bernard Buxton

Abstract: We introduce Bayes priors into a recent pixel-based, linear combination of views object recognition technique. Novel views of an object are synthesized and matched to the target scene image using numerical optimisation. Experiments on a real-image, public database with the use of two different optimisation methods indicate that the priors effectively regularize the error surface and lead to good performance in both cases. Further exploration of the parameter space has been carried out using Markov Chain Monte Carlo sampling.
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Paper Nr: 265
Title:

MULTI-DISCRIMINANT CLASSIFICATION ALGORITHM FOR FACE VERIFICATION

Authors:

Cheng H. Huang and Jhing-Fa Wang

Abstract: Linear discriminant analysis (LDA) is a conventional approach for face verification. For computing large amounts of data collected for a given face verification system, this study proposes a multi-discriminant classification algorithm to classify and verify voluminous facial images. In the training phase, the algorithm extracts all discriminant features of the training data, and classifies them as the clients’ multi-discriminant sets. The algorithm verifies a claim to the client’s multi-discriminant set, and then determines whether the claimant is the client. Comparative results demonstrate that the proposed algorithm reduces the false acceptance rate in face verification.
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Paper Nr: 271
Title:

INTRODUCING 3D VISION AND COMPUTER GRAPHICS TO ARCHAEOLOGICAL WORKFLOW - An Applicable Framework

Authors:

Hubert Mara, Andreas Monitzer and Julian Stoettinger

Abstract: Cataloging drawings of ancient vessels and sherds is still the most time consuming task in the typical archaeological workflow. The properties of these findings like profile, volume, and wall thickness have always been estimated and drawn by hand. Through archiving, classifying and exhibiting these ancient artifacts we wish to gather as precise information as possible. Within seconds, today’s 3D-scanners provide surface meshes of ancient vessels which are more precise than any manual estimation which may take up to several hours. We propose a semi-automated, applicable framework for dealing with large 3D-meshes of ancient findings from scanning the vessels for publication. In this interactive environment we estimate the axis of vessels, estimate their profile lines and render real time visualizations using state-of-the-art 3D-hardware techniques. The results can be printed in their real size for direct use in archaeological literature. Further, these methods will give the ability to publish 3D-meshes of ancient vessels for archaeological research. Recent extended tests have been carried out on archaeological sites in Peru and Austria. These experiments showed under real life circumstances the improvement of using this system in both precision and time efficiency.
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Paper Nr: 281
Title:

DETERMINATION OF THE VISUAL FIELD OF PERSONS IN A SCENE

Authors:

Adel Lablack, Frédéric Maquet and Chabane Djeraba

Abstract: The determination of the visual field for several persons in a scene is an important problem with many applications in human behavior understanding for security and customized marketing. One such application, addressed in this paper, is to catch the visual field of persons in a scene. We obtained the head pose in the image sequence manually in order to determine exactly the visual field of persons in the monitored scene. We use knowledge about the human vision, trigonometrical relations to calculate the length and the height of the visual field and quaternion approach for doing several changes of reference marks. We demonstrate this technique using a data set of videos taken by surveillance cameras on shops.
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Paper Nr: 283
Title:

BUILDING DETECTION IN IKONOS IMAGES FROM DISPARITY OF EDGES

Authors:

Charles Beumier

Abstract: The availability of very high resolution satellite images has enabled the automatic remote detection of man-made structures for applications such as damage assessment or change detection. In particular, stereo pairs of Ikonos or Quickbird images allow for the estimation of the third dimension so distinctive for buildings. Since the areas to be studied may be quite large we propose a simple, fast and possibly accurate approach for building detection. This approach consists in a three step procedure which first detects linear segments independently in the left and right images, then matches segments according to their mutual coverage, orientation and plausible disparity, and finally identifies building areas thanks to the presence of elevated segments. The solution is fast as only pixels of high gradient connected into linear segments are considered. Modelling object parts with linear segments is valid for the vast majority of man-made objects and allows for rapid segment pairing for disparity computation with possible sub-pixel accuracy. This approach has been applied to an Ikonos pair for the detection of large buildings in the context of risk assessment within GMOSS, a European Network of Excellence.
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Paper Nr: 286
Title:

TOWARDS THE ESTIMATION OF CONSPICUITY WITH VISUAL PRIORS

Authors:

Ludovic Simon, Jean-Philippe Tarel and Roland Brémond

Abstract: Traffic signs are designed to be clearly seen by drivers. However a little is known about the visual influence of the traffic sign environment on how it will be perceived. Computer estimation of the conspicuity from images using a camera mounted on a vehicle is thus of importance in order to be able to quickly make a diagnosis regarding conspicuity of traffic signs. Unfortunately, our knowledge about the human visual processing system is rather incomplete and thus conspicuity visual mechanisms remain poorly understood. A complete model for conspicuity is not known, only specific features are known to be of importance. It makes sense to assume that an important task for drivers is to search for traffic signs. We therefore propose a new paradigm for conspicuity estimation in search tasks based on statistical learning of the visual features of the object of interest.
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Paper Nr: 444
Title:

MPEG-7 DESCRIPTORS BASED CLASSIFIER FOR FACE/NON-FACE DETECTION

Authors:

Malek NADIL, Abdenour LABED and Feryel SOUAMI

Abstract: In this paper we present a high level Face/Non-face classifier which can be integrated to a content based image retrieving system. It will help to extract semantics from images prior to their retrieving. This two-steps retrieval allows reducing effects of semantic gaps on the performance of existing systems. To construct our classifier, we exploit a standardized MPEG-7 low level descriptor. Experiments performed on images taking from two data bases, showed that our technique outperforms others presented in the literature.
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Area 4 - Motion, Tracking and Stereo Vision

Full Papers
Paper Nr: 39
Title:

OPTICAL-FLOW FOR 3D ATMOSPHERIC MOTION ESTIMATION

Authors:

Patrick Héas and Etienne Mémin

Abstract: In this paper, we address the problem of estimating three-dimensional motions of a stratified atmosphere from satellite image sequences. The complexity of three-dimensional atmospheric fluid flows associated to incomplete observation of atmospheric layers due to the sparsity of cloud systems makes very difficult the estimation of dense atmospheric motion field from satellite images sequences. The recovery of the vertical component of fluid motion from a monocular sequence of image observations is a very challenging problem for which no solution exists in the literature. Based on a physically sound vertical decomposition of the atmosphere into layers of different altitudes, we propose here a dense motion estimator dedicated to the extraction of three-dimensional wind fields characterizing the dynamics of a layered atmosphere. Wind estimation is performed over the complete three-dimensional space using a multi-layer model describing a stack of dynamic horizontal layers of evolving thickness, interacting at their boundaries via vertical winds. The efficiency of our approach is demonstrated on synthetic and real sequences.
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Paper Nr: 61
Title:

SIMPLE BUT EFFECTIVE TREE STRUCTURES FOR DYNAMIC PROGRAMMING-BASED STEREO MATCHING

Authors:

Michael Bleyer and Margrit Gelautz

Abstract: This work describes a fast method for computing dense stereo correspondences that is capable of generating results close to the state-of-the-art. We propose running a separate disparity computation process in each image pixel. The idea is to root a tree graph on the pixel whose disparity needs to be reconstructed. The tree thereby forms an individual approximation of the standard four-connected grid for this specific pixel. An exact optimum of a predefined energy function on the applied tree structure is determined via dynamic programming (DP), and the root pixel is assigned to the disparity of optimal costs. We present two simple tree structures that allow for the efficient calculation of all trees’ optima with only four scanline-based DP passes. These simple trees are designed to capture all pixels of the reference frame and incorporate horizontal and vertical smoothness edges in order to weaken the scanline streaking problem inherent in DP-based approaches. We evaluate our results using the Middlebury test set. Our algorithm currently ranks at the eighth position of approximately 30 algorithms in the Middlebury database. More importantly, it is the currently best-performing method that does not use image segmentation and is significantly faster than most competing algorithms. Our method needs less than a second to determine the disparity map for typical stereo pairs.
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Paper Nr: 72
Title:

TOUCH-LESS PALM PRINT BIOMETRIC SYSTEM

Authors:

Goh O. Michael, TEE CONNIE and Teoh Beng Jin Andrew

Abstract: In this research, we propose an innovative touch-less palm print recognition system. This project is motivated by the public’s demand for non-invasive and hygienic biometric technology. For various reasons, users are concerned about touching the biometric scanners. Therefore, we propose to use a low-resolution web camera to capture the user’s hand at a distance for recognition. The users do not need to touch any device for their palm print to be extracted for analysis. A novel hand tracking and palm print region of interest (ROI) extraction technique are used to track and capture the user’s palm in real time video streams. The discriminative palm print features are extracted based on a new way that applies local binary pattern (LBP) texture descriptor on the palm print directional gradient responses. Experiments show promising result by using the proposed method. Performance can be further improved when a modified probabilistic neural network (PNN) is used for feature matching.
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Paper Nr: 78
Title:

EXPERIMENTAL EVALUATION OF RELATIVE POSE ESTIMATION ALGORITHMS

Authors:

Marcel Brückner, Ferid Bajramovic and Joachim Denzler

Abstract: We give an extensive experimental comparison of four popular relative pose (epipolar geometry) estimation algorithms: the eight, seven, six and five point algorithms. We focus on the practically important case that only a single solution may be returned by automatically selecting one of the solution candidates, and investigate the choice of error measure for the selection. We show that the five point algorithm gives very good results with automatic selection. As sometimes the eight point algorithm is better, we propose a combination algorithm which selects from the solutions of both algorithms and thus combines their strengths. We further investigate the behavior in the presence of outliers by using adaptive RANSAC, and give practical recommendations for the choice of the RANSAC parameters. Finally, we verify the simulation results on real data.
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Paper Nr: 78
Title:

EXPERIMENTAL EVALUATION OF RELATIVE POSE ESTIMATION ALGORITHMS

Authors:

Marcel Brückner, Ferid Bajramovic and Joachim Denzler

Abstract: We give an extensive experimental comparison of four popular relative pose (epipolar geometry) estimation algorithms: the eight, seven, six and five point algorithms. We focus on the practically important case that only a single solution may be returned by automatically selecting one of the solution candidates, and investigate the choice of error measure for the selection. We show that the five point algorithm gives very good results with automatic selection. As sometimes the eight point algorithm is better, we propose a combination algorithm which selects from the solutions of both algorithms and thus combines their strengths. We further investigate the behavior in the presence of outliers by using adaptive RANSAC, and give practical recommendations for the choice of the RANSAC parameters. Finally, we verify the simulation results on real data.
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Paper Nr: 91
Title:

THE ACCURACY OF SCENE RECONSTRUCTION FROM IR IMAGES BASED ON KNOWN CAMERA POSITIONS - An Evaluation with the Aid of LiDAR Data

Authors:

Stefan Lang, Marcus Hebel and Michael Kirchhof

Abstract: A novel approach for the evaluation of a 3D scene reconstruction based on LiDAR data is presented. A system for structure computation from aerial infrared imagery is described which uses known pose and position information of the sensor. Detected 2D image features are tracked and triangulated afterwards. Each estimated 3D point is assessed by means of its covariance matrix which is associated with the respective uncertainty. Finally a non-linear optimization (Gauss-Newton iteration) of 3D points yields the resulting point cloud. The obtained results are evaluated with the aid of LiDAR data. For that purpose we quantify the error of a reconstructed scene by means of a 3D point cloud acquired by a laser scanner. The evaluation procedure takes into account that the main uncertainty of a Structure from Motion (SfM) system is in direction of the line of sight. Results of both the SfM system and the evaluation are presented.
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Paper Nr: 111
Title:

REAL-TIME OBJECT DETECTION AND TRACKING FOR INDUSTRIAL APPLICATIONS

Authors:

Selim Benhimane, Hesam Najafi, Matthias Grundmann, Yakup Genc, Nassir Navab and Ezio Malis

Abstract: Real-time tracking of complex 3D objects has been shown to be a challenging task for industrial applications where robustness, accuracy and run-time performance are of critical importance. This paper presents a fully automated object tracking system which is capable of overcoming some of the problems faced in industrial environments. This is achieved by combining a real-time tracking system with a fast object detection system for automatic initialization and re-initialization at run-time. This ensures robustness of object detection, and at the same time accuracy and speed of recursive tracking. For the initialization we build a compact representation of the object of interest using statistical learning techniques during an off-line learning phase, in order to achieve speed and reliability at run-time by imposing geometric and photometric consistency constraints. The proposed tracking system is based on a novel template management algorithm which is incorporated into the ESM algorithm. Experimental results demonstrate the robustness and high precision of tracking of complex industrial machines with poor textures under severe illumination conditions.
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Paper Nr: 124
Title:

TRACK AND CUT: SIMULTANEOUS TRACKING AND SEGMENTATION OF MULTIPLE OBJECTS WITH GRAPH CUTS

Authors:

Aurélie Bugeau and Patrick Pérez

Abstract: This paper presents a new method to both track and segment multiple objects in videos using min-cut/max-flow optimizations. We introduce objective functions that combine low-level pixel-wise measures (color, motion), high-level observations obtained via an independent detection module (connected components of foreground detection masks in the experiments), motion prediction and contrast-sensitive contextual regularization. One novelty is that external observations are used without adding any association step. The minimization of these cost functions simultaneously allows ”detection-before-track” tracking (track-to-observation assignment and automatic initialization of new tracks) and segmentation of tracked objects. When several tracked objects get mixed up by the detection module (e.g., single foreground detection mask for objects close to each other), a second stage of minimization allows the proper tracking and segmentation of these individual entities despite the observation confusion. Experiments on sequences from PETS 2006 corpus demonstrate the ability of the method to detect, track and precisely segment persons as they enter and traverse the field of view, even in cases of occlusions (partial or total), temporary grouping and frame dropping.
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Paper Nr: 165
Title:

MULTI-LANE VISUAL PERCEPTION FOR LANE DEPARTURE WARNING SYSTEMS

Authors:

Juan M. Collado, Cristina Hilario, Arturo de la Escalera and Jose M. Armingol

Abstract: This paper presents a Road Detection and Tracking algorithm for Lane Departure Warning Systems. An inverse perspective transformation gives a bird-eye view of the road, where longitudinal road markings are detected by exploration of horizontal gradient, looking for a road marking model. Next, a parabolic lane model is fitted to road markings and tracked through a particle filter. The right and left lane boundaries are classified in three types (solid, broken or merge lane boundaries), through a Fourier analysis, and adjacent lanes are searched when broken or merge lines are detected. This gives the system the ability to automatically detect the number and type of road lanes. This ability allows to tell the difference between allowed and forbidden manoeuvres, such as crossing a solid line, and it is used by the lane departure warning system. Despite of its importance, lane boundary classification has been seldom considered in previous works. A Lane Departure Warning System launches an acoustic signal when a lane departure is detected. Warnings are suppressed when the blinkers are enabled, or when the vehicle is crossing a solid line regardless of the state of the blinkers.
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Paper Nr: 176
Title:

A FEATURE GUIDED PARTICLE FILTER FOR ROBUST HAND TRACKING

Authors:

Matti-Antero Okkonen, Janne Heikkilä and Matti Pietikäinen

Abstract: Particle filtering offers an interesting framework for visual tracking. Unlike the Kalman filter, particle filters can deal with non-linear and non-Gaussian problems, which makes them suitable for visual tracking in presence of real-life disturbance factors, such as background clutter and movement, fast and unpredictable object movement and unideal illumination conditions. This paper presents a robust hand tracking particle filter algorithm which exploits the principle of importance sampling with a novel proposal distribution. The proposal distribution is based on effectively calculated color blob features, propagating the particles robustly through time even in unideal conditions. In addition, a novel method for conditional color model adaptation is proposed. The experiments show that using these methods in the particle filtering framework enables hand tracking with fast movements under real world conditions.
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Paper Nr: 229
Title:

PRINCIPLED DETECTION-BY-CLASSIFICATION FROM MULTIPLE VIEWS

Authors:

Jerome Berclaz, François Fleuret and Pascal Fua

Abstract: Machine-learning based classification techniques have been shown to be effective at detecting objects in complex scenes. However, the final results are often obtained from the alarms produced by the classifiers through a post-processing which typically relies on ad hoc heuristics. Spatially close alarms are assumed to be triggered by the same target and grouped together. Here we replace those heuristics by a principled Bayesian approach, which uses knowledge about both the classifier response model and the scene geometry to combine multiple classification answers. We demonstrate its effectiveness for multi-view pedestrian detection. We estimate the marginal probabilities of presence of people at any location in a scene, given the responses of classifiers evaluated in each view. Our approach naturally takes into account both the occlusions and the very low metric accuracy of the classifiers due to their invariance to translation and scale. Results show our method produces one order of magnitude fewer false positives than a method that is representative of typical state-of-the-art approaches. Moreover, the framework we propose is generic and could be applied to any detection-by-classification task.
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Paper Nr: 230
Title:

STRUCTURE FROM OMNIDIRECTIONAL STEREO RIG MOTION FOR CITY MODELING

Authors:

Michal Havlena, Tomas Pajdla and Kurt Cornelis

Abstract: This paper deals with a step towards a 3D reconstruction system for city modeling from omnidirectional video sequences using structure from motion together with stereo constraints. We concentrate on two issues. First, we show how the tracking and reconstruction paradigm were adapted to use omnidirectional images taken by lenses with 180 degrees field of view. This concerns mainly camera calibration transforming the pixel locations into rays and solving the minimal problem for 3D-to-2D matches using RANSAC. Secondly, we compare the results of the reconstruction using additional stereo constraints to the results when these constraints are not used and show that they are needed to make the reconstruction stable. Performance of the system is demonstrated on a sequence of 870 images acquired while driving in a city.
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Paper Nr: 231
Title:

3D HUMAN FACE MODELLING FROM UNCALIBRATED IMAGES USING SPLINE BASED DEFORMATION

Authors:

Nikos Barbalios, Nikolaos Nikolaidis and Ioannis Pitas

Abstract: Accurate and plausible 3D face reconstruction remains a difficult problem up to this day, despite the tremendous advances in computer technology and the continuous growth of the applications utilizing 3D face models (e.g. biometrics, movies, gaming). In this paper, a two-step technique for efficient 3D face reconstruction from a set of face images acquired using an uncalibrated camera is presented. Initially, a robust structure from motion (SfM) algorithm is applied over a set of manually selected salient image features to retrieve an estimate of their 3D coordinates. These estimates are further utilized to deform a generic 3D face model, using smoothing splines, and adapt it to the characteristics of a human face.
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Paper Nr: 267
Title:

KLT TRACKING USING INTRINSIC AND EXTRINSIC CAMERA PARAMETERS IN CONSIDERATION OF UNCERTAINTY

Authors:

Michael Trummer, Joachim Denzler and Christoph Munkelt

Abstract: Feature tracking is an important task in computer vision, especially for 3D reconstruction applications. Such procedures can be run in environments with a controlled sensor, e.g. a robot arm with camera. This yields the camera parameters as special knowledge that should be used during all steps of the application to improve the results. As a first step, KLT (Kanade-Lucas-Tomasi) tracking (and its variants) is an approach widely accepted and used to track image point features. So, it is straightforward to adapt KLT tracking in a way that camera parameters are used to improve the feature tracking results. The contribution of this work is an explicit formulation of the KLT tracking procedure incorporating known camera parameters. Since practical applications do not run without noise, the uncertainty of the camera parameters is regarded and modeled within the procedure. Comparing practical experiments have been performed and the results are presented.
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Paper Nr: 282
Title:

A MAXIMUM LIKELIHOOD SURFACE NORMAL ESTIMATION ALGORITHM FOR HELMHOLTZ STEREOPSIS

Authors:

Jean-Yves Guillemaut, Ondrej Drbohlav, John Illingworth and Radim Sara

Abstract: Helmholtz stereopsis is a relatively recent reconstruction technique which is able to reconstruct scenes with arbitrary and unknown surface reflectance properties. Conventional implementations of the method estimate surface normal direction at each surface point via an eigenanalysis, thereby optimising an algebraic distance. We develop a more physically meaningful radiometric distance whose minimisation is shown to yield a Maximum Likelihood surface normal estimate. The proposed method produces more accurate results than algebraic methods on synthetic imagery and yields excellent reconstruction results on real data. Our analysis explains why, for some imaging configurations, a sub-optimal algebraic distance can yield good results.
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Paper Nr: 318
Title:

PROBABILISTIC APPEARANCE-BASED NAVIGATION OF A MOBILE ROBOT - Controlling a Robot in Route Following

Authors:

Luis Paya, Oscar Reinoso, Arturo Gil, M. Asunción Vicente and Jose L. Aznar

Abstract: In this work, a solution to the problem of multi-robot following routes is proposed using an appearance-based method. In this approach, several images are stored along the route to follow, using an uncalibrated forward-looking camera. To extract the most relevant information, an incremental PCA process has been implemented. This incremental process allows adding new locations to the PCA database without necessity of creating it from the scratch. Then, the follower robots can follow the route while a leader one is still recording it. These follower robots, using this database, make first an auto-location process to know their current position and then a control phase to compute the necessary steering speed to tend to the route and follow it till the end. Both speeds are obtained also through the visual information in an appearance-based approach. The problem of ‘visual aliasing’, typical in office environments, is avoided with a probabilistic approach that, using a Markov-process model, makes the localization more robust. The experimental results have shown how this is a simple but robust and powerful approach for routes in an office environment.
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Paper Nr: 323
Title:

AUTONOMOUS MODEL-BASED OBJECT IDENTIFICATION & CAMERA POSITION ESTIMATION WITH APPLICATION TO AIRPORT LIGHTING QUALITY CONTROL

Authors:

James Niblock, Jian-Xun Peng, Karen Mcmenemy and George W. Irwin

Abstract: The development of an autonomous system for the accurate measurement of the quality of aerodrome ground lighting (AGL) in accordance with current standards and recommendations is presented. The system is composed of an imager which is placed inside the cockpit of an aircraft to record images of the AGL during a normal descent to an aerodrome. Before the performance of the AGL is assessed, it is first necessary to uniquely identify each luminaire within the image and track it through the complete image sequence. A model-based (MB) methodology is used to ascertain the optimum match between a template of the AGL and the actual image data. Projective geometry, in addition to the image and real world location of the extracted luminaires, is then used to calculate the position of the camera at the instant the image was acquired. Algorithms are also presented which model the distortion apparent within the sensors optical system and average the camera’s intrinsic parameters over multiple frames, so as to minimise the effects of noise on the acquired image data and hence make the camera’s estimated position and orientation more accurate. The positional information is validated using actual approach image data.
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Short Papers
Paper Nr: 20
Title:

AUTOMATIC INITIALIZATION FOR BODY TRACKING - Using Appearance to Learn a Model for Tracking Human Upper Body Motions

Authors:

Joachim Schmidt and Modesto Castrillon

Abstract: Social robots require the ability to communicate and recognize the intention of a human interaction partner. Humans commonly make use of gestures for interactive purposes. For a social robot, recognition of gestures is therefore a necessary skill. As a common intermediate step, the pose of an individual is tracked over time making use of a body model. The acquisition of a suitable body model, i.e. self-starting the tracker, however, is a complex and challenging task. This paper presents an approach to facilitate the acquisition of the body model during interaction. Taking advantage of a robust face detection algorithm provides the opportunity for automatic and markerless acquisition of a 3D body model using a monocular color camera. For the given human robot interaction scenario, a prototype has been developed for a single user configuration. It provides automatic initialization and failure recovery of a 3D body tracker based on head and hand detection information, delivering promising results.
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Paper Nr: 36
Title:

IMAGE SEQUENCE STABILIZATION USING FUZZY KALMAN FILTERING AND LOG-POLAR TRANSFORMATION

Authors:

Nikolaos Kyriakoulis, Antonios Gasteratos and Angelos Amanatiadis

Abstract: Digital image stabilization (DIS) is the process that compensates the undesired fluctuations of a frame’s position in an image sequence by means of digital image processing techniques. DIS techniques usually comprise two successive units. The first one estimates the motion and the successive one compensates it. In this paper, a novel digital image stabilization technique is proposed, which is featured with a fuzzy Kalman estimation of the global motion vector in the log-polar plane. The global motion vector is extracted using four local motion vectors computed on respective sub-images in the log-polar plane. The proposed technique exploits both the advantages of the fuzzy Kalman system and the log-polar plane. The compensation is based on the motion estimation in the log-polar domain, filtered by the fuzzy Kalman system. The described technique outperforms in terms of response times, the output quality and the level of compensation.
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Paper Nr: 52
Title:

TOWARDS EUCLIDEAN RECONSTRUCTION FROM VIDEO SEQUENCES

Authors:

Dimitri Bulatov

Abstract: This paper presents two algorithms needed to perform a dense 3D-reconstruction from video streams recorded with uncalibrated cameras. Our algorithm for camera self-calibration makes extensive use of the constant focal length. Furthermore, a fast dense reconstruction can be performed by fusion of tessellations obtained from different sub-sequences (LIFT). Moreover, we will present our system for performing the reconstruction in a projective coordinate system. Since critical motions are common in the majority of practical situations, care has been taken to recognize and deal with them.
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Paper Nr: 58
Title:

BACKGROUND SUBTRACTION WITH ADAPTIVE SPATIO-TEMPORAL NEIGHBORHOOD ANALYSIS

Authors:

Marco Cristani and Vittorio Murino

Abstract: In the literature, visual surveillance methods based on joint pixel and region analysis for background subtraction are proven to be effective in discovering foreground objects in cluttered scenes. Typically, per-pixel foreground detection is contextualized in a local neighborhood region in order to limit false alarms. However, such methods have an heavy computational cost, depending on the size of the surrounding region considered for each pixel. In this paper, we propose an original and efficient joint pixel-region analysis technique able to automatically select the sampling rate with which pixels in different areas are checked out, while adapting the size of the neighborhood region considered. The algorithm has been validated on standard videos with benchmark tests, proving the goodness of the approach, especially in terms of quality of the detection with respect to the frame rate achieved.
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Paper Nr: 58
Title:

BACKGROUND SUBTRACTION WITH ADAPTIVE SPATIO-TEMPORAL NEIGHBORHOOD ANALYSIS

Authors:

Marco Cristani and Vittorio Murino

Abstract: In the literature, visual surveillance methods based on joint pixel and region analysis for background subtraction are proven to be effective in discovering foreground objects in cluttered scenes. Typically, per-pixel foreground detection is contextualized in a local neighborhood region in order to limit false alarms. However, such methods have an heavy computational cost, depending on the size of the surrounding region considered for each pixel. In this paper, we propose an original and efficient joint pixel-region analysis technique able to automatically select the sampling rate with which pixels in different areas are checked out, while adapting the size of the neighborhood region considered. The algorithm has been validated on standard videos with benchmark tests, proving the goodness of the approach, especially in terms of quality of the detection with respect to the frame rate achieved.
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Paper Nr: 67
Title:

A FAST POST-PROCESSING TECHNIQUE FOR REAL-TIME STEREO CORRESPONDENCE

Authors:

Georgios - Tsampikos Michailidis, Leonidas Kotoulas and Ioannis Andreadis

Abstract: In computer vision, the extraction of dense and accurate disparity maps is a computationally expensive and challenging problem, and high quality results typically require from several seconds to several minutes to be obtained. In this paper, we present a new post-processing technique, which detects the incorrect reconstructed pixels after the initial matching process and replaces them with correct disparity values. Experimental results with Middlebury data sets show that our approach can process images of up to 3MPixels in less than 3.3 msec, producing at the same time semi-dense (up to 99%) and accurate (up to 94%) disparity maps. We also propose a way to adaptively change, in real time, the density and the accuracy of the extracted disparity maps. In addition, the matching and post-processing procedures are calculated without using any multiplication, which makes the algorithm very fast, while its reduced complexity simplifies its implementation. Finally, we present the hardware implementation of the proposed algorithm.
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Paper Nr: 77
Title:

USING LOW-LEVEL MOTION TO ESTIMATE GAIT PHASE

Authors:

Ben Daubney, David Gibson and Neill Campbell

Abstract: This paper presents a method that is capable of robustly estimating gait phase of a human walking using the motion of a sparse cloud of feature points extracted using a standard feature tracker. We first learn statistical motion models of the trajectories we would expect to observe for each of the main limbs. By comparing the motion of the tracked features to our models and integrating over all features we create a state probability matrix that represents the likelihood of being at a particular phase as a function of time. By using dynamic programming and allowing only likely phase transitions to occur between consecutive frames, an optimal solution can be found that estimates the gait phase for each frame. This work demonstrates that despite the sparsity and noise contained in the tracking data, the information encapsulated in the motion of these points is sufficient to extract gait phase to a high level of accuracy. Presented results demonstrate our system is robust to changes in height of the walker, gait frequency and individual gait characteristics.
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Paper Nr: 90
Title:

A SLAG TEMPERATURE AND FLOW MONITORING SYSTEM

Authors:

Jean-Philippe Andreu

Abstract: Quality assessment of steel processing essentially relies on the continuous monitoring and control of the steel temperature and the flow patterns of the molten material. Among the various sensors developed to control that process, CCD cameras emerge as a good alternative to more classical measuring devices. Multi-spectral imaging systems based on cameras working in the visible spectrum offer a viable alternative to high cost thermographic infrared cameras. This paper presents a slag monitoring system based on dual wavelength thermographic cameras. The system allows a real-time and contactless monitoring of the slag temperature and a continuous monitoring of the flow patterns of the ingot slag topping in order to assess the quality of the produced steel.
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Paper Nr: 121
Title:

3D ARTICULATED HAND TRACKING BY NONPARAMETRIC BELIEF PROPAGATION ON FEASIBLE CONFIGURATION SPACE

Authors:

Tangli Liu, Wei Liang and Yunde Jia

Abstract: An efficient articulated hand tracking method underlying the 3D graphical model from monocular image sequences is proposed in this paper. Due to the inaccurate dependences among the components of human hand leading to distorted estimates in previous work, we design a pertinence graphical model combined with domain–specific heuristics among the components of human hand describing the hand’s 3D structure, kinematics, and dynamics. The proposed model decomposes multivariate, joint distributions into a set of local interactions among small subsets. The modular structure provides an intuitive language for expressing domain–specific knowledge about the variable relationships, and facilitates tracking each hand component independently. And then, we provide a novel belief propagation algorithm to inference in hand graphical model. The algorithm can accommodate an extremely broad class of potential functions besides the potentials appropriate for our model. The experimental results show the robustness and efficiency of tracking each hand component.
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Paper Nr: 154
Title:

DETECTING, TRACKING AND COUNTING FISH IN LOW QUALITY UNCONSTRAINED UNDERWATER VIDEOS

Authors:

Concetto Spampinato, Chen-Burger Yun-Heh, Nadarajan Gayathri and Robert Fisher

Abstract: In this work a machine vision system capable of analysing underwater videos for detecting, tracking and counting fish is presented. The real-time videos, collected near the Ken-Ding sub-tropical coral reef waters are managed by EcoGrid, Taiwan and are barely analysed by marine biologists. The video processing system consists of three subsystems: the video texture analysis, fish detection and tracking modules. Fish detection is based on two algorithms computed independently, whose results are combined in order to obtain a more accurate outcome. The tracking was carried out by the application of the CamShift algorithm that enables the tracking of objects whose numbers may vary over time. Unlike existing fish-counting methods, our approach provides a reliable method in which the fish number is computed in unconstrained environments and under several scenarios (murky water, algae on camera lens, moving plants, low contrast, etc.). The proposed approach was tested with 20 underwater videos, achieving an overall accuracy as high as 85%.
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Paper Nr: 189
Title:

DEPTH PREDICTION AT HOMOGENEOUS IMAGE STRUCTURES

Authors:

Sinan Kalkan, Florentin Woergoetter and Norbert Krueger

Abstract: This paper proposes a voting-based model that predicts depth at weakly-structured image areas from the depth that is extracted using a feature-based stereo method. We provide results, on both real and artificial scenes, that show the accuracy and robustness of our approach. Moreover, we compare our method to different dense stereo algorithms to investigate the effect of texture on performance of the two different approaches. The results confirm the expectation that dense stereo methods are suited better for textured image areas and our method for weakly-textured image areas.
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Paper Nr: 194
Title:

CORRELATION ICP ALGORITHM FOR POSE ESTIMATION BASED ON LOCAL AND GLOBAL FEATURES

Authors:

Marco C. Fabila and Gerald Sommer

Abstract: In this paper we present a new variant of ICP (iterative closest point) algorithm based on local feature correlation. Our approach combines global and local feature information to find better correspondence sets and to use them to compute the 3D pose of the object model even for the case of large displacements between model and image data. For such cases, we propose a 2D alignment in the image plane (rotation plus translation) before the feature extraction process. This has some advantages over the classical methods like better convergence and robustness. Furthermore, it avoids the need of a normal pre-alignment step in 3D. Our approach was tested on synthetical and real-world data to compare the convergence behavior and performance against other versions of the ICP algorithm combined with a classical pre-alignment approach.
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Paper Nr: 213
Title:

A NEW SET OF FEATURES FOR ROBUST CHANGE DETECTION

Authors:

José Sigut, Sid-Ahmed Ould Sidha, Juan Díaz and Carina González

Abstract: A new set of features for robust change detection is proposed. These features are obtained from a transformation of the thresholded intensity difference image. Their performance is tested on two video sequences acquired in a human-machine interaction scenario under very different illumination conditions. Several performance measures are computed and a comparison with other well known classical change detection methods is done. The performed experiments show the effectiveness and robustness of our proposal.
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Paper Nr: 218
Title:

EXACT VISUAL HULL FROM MARCHING CUBES

Authors:

CHEN LIANG and Kwan-Yee K. Wong

Abstract: The marching cubes algorithm has been widely adopted for extracting a surface mesh from a volumetric description of the visual hull reconstructed from silhouettes. However, typical volumetric descriptions, such as an octree, provide only a binary description about the visual hull. The lack of interpolation information along each voxel edge, which is required by the marching cubes algorithm, usually results in inaccurate and bumpy surface mesh. In this paper, we propose a novel method to efficiently estimate the exact intersections between voxel edges and the visual hull boundary, which replace the missing interpolation information. The method improves both the visual quality and accuracy of the estimated visual hull mesh, while retaining the simplicity and robustness of the volumetric approach. To verify this claim, we present both synthetic and real-world experiments, as well as comparisons with existing volumetric approaches and other approaches targeting at an exact visual hull reconstruction.
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Paper Nr: 219
Title:

ROBUST MULTI-TARGET TRACKING USING MEAN SHIFT AND PARTICLE FILTER WITH TARGET MODEL UPDATE

Authors:

Hong Liu, Jintao Li, Yueliang Qian and Qun Liu

Abstract: We propose a novel multiple targets tracking algorithm combining Mean Shift and Particle Filter, and enhance the performance with target model update process. Mean Shift has a low complexity, but is weak in dealing with multi-modal probability density functions (pdfs). Particle Filter is robust to the partial occlusion and can deal with multi-modal pdfs. In real application, illumination conditions, the visual angle as well as object occlusion can change target appearance, thus influence the quality of Particle Filter. For multi-target tracking task, the mutual occlusion of targets and computational complexity are important problems for tracking system. In this paper, Mean Shift algorithm is embedded into Particle Filter framework to get stable tracking and reduce computational load. To overcome the target appearance changes caused by illumination changes and object occlusion, targets model are updated adaptively during tracking. Experimental results show that our tracking system can robustly track multiple targets with mutual occlusion and correctly maintain their identities with smaller number of particles than Particle Filter.
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Paper Nr: 272
Title:

CALIBRATION-FREE EYE GAZE DIRECTION DETECTION WITH GAUSSIAN PROCESSES

Authors:

Basilio Noris, Karim Benmachiche and Aude G. Billard

Abstract: In this paper we present a solution for eye gaze detection from a wireless head mounted camera designed for children aged between 6 months and 18 months. Due to the constraints of working with very young children, the system does not seek to be as accurate as other state-of-the-art eye trackers, however it requires no calibration process from the wearer. Gaussian Process Regression and Support Vector Machines are used to analyse the raw pixel data from the video input and return an estimate of the child’s gaze direction. A confidence map is used to determine the accuracy the system can expect for each coordinate on the image. The best accuracy so far obtained by the system is 2.34◦ on adult subjects, tests with children remain to be done.
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Paper Nr: 284
Title:

LUCAS-KANADE INVERSE COMPOSITIONAL USING MULTIPLE BRIGHTNESS AND GRADIENT CONSTRAINTS

Authors:

Ahmed Fahad and Tim Morris

Abstract: A recently proposed fast image alignment algorithm is the inverse compositional algorithm based on Lucas-Kanade. In this paper, we present an overview of different brightness and gradient constraints used with the inverse compositional algorithm. We also propose an efficient and robust data constraint for the estimation of global motion from image sequences. The constraint combines brightness and gradient constraints under multiple quadratic errors. The method can accommodate various motion models. We concentrate on the global efficiency of the constraint in capturing the global motion for image alignment. We have applied the algorithm to various test sequences with ground truth. From the experimental results we conclude that the new constraint provides reduced motion error at the expense of extra computations.
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Paper Nr: 292
Title:

VIEW-BASED ROBOT LOCALIZATION USING ILLUMINATION-INVARIANT SPHERICAL HARMONICS DESCRIPTORS

Authors:

Holger Friedrich, David Dederscheck, Martin Mutz and Rudolf Mester

Abstract: In this work we present a view-based approach for robot self-localization using a hemispherical camera system. We use view descriptors that are based upon Spherical Harmonics as orthonormal basis functions on the sphere. The resulting compact representation of the image signal enables us to efficiently compare the views taken at different locations. With the view descriptors stored in a database, we compute a similarity map for the current view by means of a suitable distance metric. Advanced statistical models based upon principal component analysis introduced to that metric allows to deal with severe illumination changes, extending our method to real-world applications.
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Paper Nr: 293
Title:

MEASUREMENT NOISE IN PHOTOMETRIC STEREO BASED SURFACE RECONSTRUCTION

Authors:

Toni Kuparinen, Ville Kyrki and Pekka Toivanen

Abstract: In this paper, we present a noise reduction method for photometric stereo based surface reconstruction of surfaces with high frequency height variation. Such surfaces are important for many industrial settings, for example, in paper and textile manufacturing. The paper presents the derivation of the effect of white image noise to gradient fields. Based on the derivation, a denoising approach of the gradient fields using the Wiener filter is proposed. Several known surface reconstruction methods with and without the proposed denoising approach are evaluated experimentally, with respect to the effect of the noise, and the boundary conditions of the reconstruction. The experimental results validate that the proposed approach improves the surface reconstruction on surfaces with high frequency height variation.
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Paper Nr: 305
Title:

OMNIDIRECTIONAL CAMERA MOTION ESTIMATION

Authors:

Akihiko Torii and Tomas Pajdla

Abstract: We present an automatic technique for computing relative camera motion and simultaneous omnidirectional image matching. Our technique works for small as well as large motions, tolerates multiple moving objects and very large occlusions in the scene. We combine three principles and obtain a practical algorithm which improves the state of the art. First, we show that the correct motion is found much sooner if the tentative matches are sampled after ordering them by the similarity of their descriptors. Secondly, we show that the correct camera motion can be better found by soft voting for the direction of the motion than by selecting the motion that is supported by the largest set of matches. Finally, we show that it is useful to filter out the epipolar geometries which are not generated by points reconstructed in front of cameras. We demonstrate the performance of the technique in an experiment with 189 image pairs acquired in a city and in a park. All camera motions were recovered with the error of the motion direction smaller than 8◦, which is 4 % of the 183◦ field of view, w.r.t. the ground truth.
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Paper Nr: 329
Title:

MULTI-CAMERA DETECTION AND MULTI-TARGET TRACKING - Traffic Surveillance Applications

Authors:

Ralf Reulke, Sascha Bauer, Thomas Döring and Robert Spangenberg

Abstract: Non-intrusive video-detection for traffic flow observation and surveillance is the primary alternative to conventional inductive loop detectors. Video Image Detection Systems (VIDS) can derive traffic parameters by means of image processing and pattern recognition methods. Existing VIDS emulate the inductive loops. We propose a trajectory based recognition algorithm to expand the common approach and to obtain new types of information (e.g. queue length or erratic movements).Different views of the same area by more than one camera sensor are necessary, because of the typical limitations of single camera systems, resulting from occlusions by other cars, trees and traffic signs. A distributed cooperative multi-camera system enables a significant enlargement of the observation area. The trajectories are derived from multi-target tracking. The fusion of object data from different cameras will be done by a tracking approach. This approach opens up opportunities to identify and specify traffic objects, their location, speed and other characteristic object information. The system creates new derived and consolidated information of traffic participants. Thus, also descriptions of individual traffic participants are possible.
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Paper Nr: 348
Title:

RANDOM FOREST CLASSIFIERS FOR REAL-TIME OPTICAL MARKERLESS TRACKING

Authors:

Iñigo Barandiaran, Charlotte Cottez, Céline Paloc and Manuel Graña

Abstract: Augmented reality (AR) is a very promising technology that can be applied in many areas such as healthcare, broadcasting or manufacturing industries. One of the bottlenecks of such application is a robust real-time optical markerless tracking strategy. In this paper we focus on the development of tracking by detection for plane homography estimation. Feature or keypoint matching is a critical task in such approach. We propose to apply machine learning techniques to solve this problem. We present an evaluation of an optical tracking implementation based on Random Forest classifier. The implementation has been successfully applied to indoor and outdoor augmented reality design review application.
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Paper Nr: 381
Title:

ESTIMATING VEHICLE VELOCITY USING RECTIFIED IMAGES

Authors:

Cristina Maduro, Katherine Batista, Paulo Peixoto and Jorge Batista

Abstract: In this paper we propose a technique to estimate vehicles velocity, using rectified images that represent a top view of the highway. To rectify image sequences captured by uncalibrated cameras, this method automatically estimates two vanishing points using lines from the image plane. This approach requires two known lengths on the ground plane and can be applied to highways that are fairly straight near the surveillance camera. Once the background image is rectified it is possible to locate the stripes and boundaries of the highway lanes. This process may also be used to count vehicles, estimate their velocities and the mean velocity associated to each of the previously identified highway lanes.
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Paper Nr: 13
Title:

TRAFFIC SURVEILLANCE USING GABOR FILTER BANK AND KALMAN PREDICTOR

Authors:

Mehmet Celenk, James Graham and Santosh Singh

Abstract: This paper builds upon our earlier work by applying an optimized version of our non-linear scene prediction method to traffic surveillance video. As previously, a Gabor-filter bank has been selected as a primary detector for any changes in a given image sequence. The detected ROI (region of interest) in arbitrary motion is fed to a non-linear Kalman filter for predicting the next scene in time-varying video, which is subject to prediction error invalidation. Potential applications of this research are mainly in the areas of traffic control and monitoring, traffic flow surveillance, and MPEG video-compression. The reported experimental results show improved performance over the non-linear Kalman filtering based scene prediction results in our previous work. The low least mean square error (LMSE), on the average of about 2 to 3 % remains close to the average reported in our earlier work, however, the fluctuations in error have disappeared, proving the reliability of the approach to traffic-motion prediction.
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Paper Nr: 29
Title:

POSE ESTIMATION FROM LINES BASED ON THE DUAL-NUMBER METHODS

Authors:

Caixia Zhang, Zhanyi Hu and Fengmei Sun

Abstract: It is a classical problem to estimate the camera pose from a calibrated image of 3D entities (points or lines) in computer vision. According to the coplanarity of the corresponding image line and space line, a new group of constraints is introduced based on the dual-number methods. Different from the existing methods based on lines, we do not use an isolated point on either the space line or the image line, but the whole line data. Thus, it is evitable to detect the corner as well as the corresponding propagating error.
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Paper Nr: 30
Title:

MODEL-FREE MARKERLESS TRACKING FOR REMOTE SUPPORT IN UNKNOWN ENVIRONMENTS

Authors:

Alexander Ladikos, Selim Benhimane, Nassir Navab and Mirko Appel

Abstract: We propose a complete system that performs real-time markerless tracking for Augmented Reality-based remote user support in a priori unknown environments. In contrast to existing systems, which require a prior setup and/or knowledge about the scene, our system can be used without preparation. This is due to our tracking algorithm which does not need a 3D-model of the scene or a learning-phase for the initialization. This allows us to perform fast and robust markerless tracking of the objects which are to be augmented. The proposed solution does not require artificial markers or special lighting conditions. The only requirement is the presence of locally planar objects in the scene, which is true for almost every man-made structure and in particular technical installations. The augmentations are chosen by a remote expert who is connected to the user over a network and receives a live stream of the scene.
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Paper Nr: 41
Title:

GLOBAL DEPTH ESTIMATION FOR MULTI-VIEW VIDEO CODING USING CAMERA PARAMETERS

Authors:

Xiaoyun Zhang, Weile Zhu, Weile Zhu, Weile Zhu, George Yang, George Yang and George Yang

Abstract: Multi-view video plus depth (MVD) data format for Multi-view Video Coding (MVC) can support rendering a wide range continuum of views at the decoder for advanced 3DV and FVV systems. Thus, it is important to study global depth to reduce the rate for depth side information and to improve depth search efficiency. In this paper, we propose a global depth estimation algorithm from multi-view images using camera parameters. First, an initial depth is obtained from the convergent point of the camera system by solving a set of linear equations. Then, the global depth is searched around the initial depth to minimize the absolute difference between the synthesized view and the practical view. Because the initial depth can provide appropriate depth search range and step size, the global depth can be estimated efficiently and quickly with less computation. Experimental results verify the algorithm performance.
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Paper Nr: 102
Title:

IMPLEMENTATION OF REAL-TIME VISUAL TRACKING SYSTEM FOR AIRBORNE TARGETS

Authors:

Muhammad A. Memon, Furqan Muhammad Khan, Farrukh Hussain Khan, Muhammad Anees Rana and Omair Abdul Rahman

Abstract: A real-time visual tracking system is presented for tracking airborne targets. The algorithm is based on intensity difference between background and the target in a gray-scale frame. As the background is uniform for videos of airborne targets, decision is made on contrast between tracking gate boundary and the target inside that rectangular gate. The algorithm is embedded on DSP Starter Kit (DSK) 6713 and a 586 embedded controller is used for servo control and processing. A personal computer (PC) provides the user interface for the system. The performance of the system is verified with different airborne targets from birds to helicopters and its reliability and constraints are presented.
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Paper Nr: 123
Title:

A NOVEL EVOLUTIONARY FRAMEWORK FOR FEATURE MATCHING

Authors:

Biao Wang and Chaoying Tang

Abstract: The paper presents a new feature matching scheme based on the Queen-bee Evolution for two uncalibrated images. Matching features needs an exhaustive search in a vast space, for which evolutionary algorithms are recommended recently. This paper propose a simple and effective algorithm. We intuitively encode a string of integer numbers assigned to the features as chromosomes and develop a novel crossover operator respectively which can preserve the position information without any disruption. We also tailor swap mutation operator to prevent from premature convergence and invalid solutions. As a result, the proposed algorithm can quickly achieve the global or near global optimal solution cooperating with the linear ranking selection and the elitist replacement. Meanwhile, it is a more general framework for matching various types of features. The experimental results illustrate the performance of the proposed approach.
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Paper Nr: 182
Title:

BIOLOGICALLY INSPIRED ATTENTIVE MOTION ANALYSIS FOR VIDEO SURVEILLANCE

Authors:

Florian Raudies and Heiko Neumann

Abstract: Recently proposed algorithms in the field of vision-based video surveillance are build upon directionally consistent flow (Wixson and Hansen, 1999; Tian and Hampapur, 2005), or statistics of foreground and background (Ren et al., 2003; Zhang et al., 2007). Here, we present a novel approach which utilizes an attention mechanism to focus processing on (highly) suspicious image regions. The attention signal is generated through temporal integration of localized image features from monocular image sequences. This approach incorporates biologically inspired mechanisms, for feature extraction and spatio-temporal grouping. We compare our approach with an existing method for the task of video surveillance (Tian and Hampapur, 2005) with a receiver operator characteristic (ROC) analysis. In conclusion our model is shown to yield results which are comparable with existing approaches.
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Paper Nr: 216
Title:

HAND GESTURE TRACKING FOR WEARABLE COMPUTING SYSTEMS

Authors:

Xiujuan Chai, Kongqiao Wang, Luosi Wei and Hao Wang

Abstract: Wearable computing is a hot research field in recent years. For the important role in wearable computing systems, hand gesture tracking attracts many researchers’ interests. This paper proposes a simple but efficient temporal differencing based hand motion tracking scheme which is used to build an augmented drumming system. In our method, the accurate motion information is gotten by a fine-coarse-fine strategy. Once getting the motion region candidates, a skin detector based on skin colour histogram is used to determine which region is our concerned hand. In the tracking procedure, motion direction constraint is also adopted in order to get a robust result. Different with the traditional skin detection for the whole image frame, combining with the motion region detection, the hand detection is no longer effected by the skin-like background. Experimental results show that our presented hand gesture tracking is robust and fast. We also adopt it into an augmented drumming system to show the good performance and powerful potential of our method in wearable computing systems.
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Paper Nr: 223
Title:

A PDES METHOD PRESERVING BOUNDARIES ON DENSE DISPARITY MAP RECONSTRUCTION

Authors:

Ji liu, Junjian Peng, Yuechao Wang and Yandong Tang

Abstract: Over smoothness restricts the application of PDEs in the field of dense disparity map reconstruction, because disparity map reconstruction usually requires preserving discontinuousness in some areas such as the boundaries of objects. To preserve disparity discontinuousness, this paper adopts two strategies. Firstly, ground control points (GCPs) are introduced as the soft constraint. Secondly, this paper designs a structure of smoothness part in energy functional, which can preserve discontinuousness effectively. Moreover, the adjustable parameters in the smoothness part advance its robustness. In experiments, we compare proposed method with graph cuts method and prove that PDEs is also a useful solution for disparity map reconstruction and has the advantage of dealing with smooth images.
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Paper Nr: 268
Title:

FEATURE SETS FOR PEOPLE AND LUGGAGE RECOGNITION IN AIRPORT SURVEILLANCE UNDER REAL-TIME CONSTRAINTS

Authors:

Juan Rosell, Gabriela Andreu, Ángel Rodas, Vicente Atienza and Jose Valiente

Abstract: We study two different sets of features with the aim of classifying objects from videos taken in an airport. Objects are classified into three different classes: single person, group of people, and luggage. We have used two different feature sets, one set based on classical geometric features, and another based on average density of foreground pictures in areas of the blobs. In both cases, easily computed features were selected because our system must run under real-time constraints. During the development of the algorithms, we also studied if shadows affect the classification rate of objects. We achieved this by applying two shadow removal algorithms to estimate the usefulness of such techniques under real-time constraints.
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Paper Nr: 295
Title:

AN EFFICIENT SENSOR FOR TRAFFIC MONITORING AND TRACKING APPLICATIONS - Based on Fast Motion Detection at the Areas of Interest

Authors:

Nikolaos Zournis-Karouzos, Alexandra Koutsia, Kosmas Dimitropoulos and Nikos Grammalidis

Abstract: We propose a novel video sensor for real-time motion detection at specific user-defined regions of interest, designed primarily for traffic monitoring, surveillance and tracking applications. Specifically, the new sensor a) supports virtual detectors with a generalized (polygonal) shape, thus providing additional flexibility in the design of detector configurations, b) is based on fast implementations of recent state-of-the art background extraction and update techniques and c) constitutes a generic, inexpensive software solution, which can be used with any video camera. First experimental results confirm that the new video sensor meets the expectations in terms of real-time performance and demonstrates the additional functionalities, according to which it was designed. The final goal is to use this new sensor as an alternative, improved version of embedded motion detection video sensors (like Autoscope®).
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Paper Nr: 363
Title:

CAMERA MOTION ESTIMATION USING PARTICLE FILTERS

Authors:

Symeon Nikitidis, Stefanos Zafeiriou and Ioannis Pitas

Abstract: In this paper a novel algorithm for estimating the parametric form of the camera motion is proposed. A novel stochastic vector field model is presented which can handle smooth motion patterns derived from long periods of stable camera movement and also can cope with rapid motion changes and periods where camera remains still. A set of rules for robust and online updating of the model parameters is also proposed, based on the Expectation Maximization algorithm. Finally, we fit this model in a particle filters framework, in order to predict the future camera motion based on current and prior knowledge.
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Paper Nr: 365
Title:

ITERATIVE RIGID BODY TRANSFORMATION ESTIMATION FOR VISUAL 3-D OBJECT TRACKING

Authors:

Micha Hersch, Thomas Reichert and Aude Billard

Abstract: We present a novel yet simple 3D stereo vision tracking algorithm which computes the position and orientation of an object from the location of markers attached to the object. The novelty of this algorithm is that it does not assume that the markers are tracked synchronously. This provides a higher robustness to the noise in the data, missing points and outliers. The principle of the algorithm is to perform a simple gradient descent on the rigid body transformation describing the object position and orientation. This is proved to converge to the correct solution and is illustrated in a simple experimental setup involving two USB cameras.
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Paper Nr: 375
Title:

ANOMALY DETECTION WITH LOW-LEVEL PROCESSES IN VIDEOS

Authors:

Ákos Utasi and László Czúni

Abstract: In our paper we deal with the problem of low-level motion modelling and unusual event detection in urban surveillance videos. We model the direction of optical flow vectors at image pixels. We implemented and tested probability based approaches such as probability estimation, Mixture of Gaussians modelling, and spatial averaging (with Mean-shift segmentation). We propose a Markovian prior to get reliable spatio-temporal support. We tested the techniques on synthetic and real video sequences.
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Paper Nr: 402
Title:

LONG-TERM VS. GREEDY ACTION PLANNING FOR COLOR LEARNING ON A MOBILE ROBOT

Authors:

Mohan Sridharan and Peter Stone

Abstract: A major challenge to the deployment of mobile robots is the ability to function autonomously, learning appropriate models for environmental features and adapting those models in response to environmental changes. This autonomous operation in turn requires autonomous selection/planning of an action sequence that facilitates learning and adaptation. Here we focus on color modeling/learning and analyze two algorithms that enable a mobile robot to plan action sequences that facilitate color learning: a long-term action selection approach that maximizes color learning opportunities while minimizing localization errors over an entire action sequence, and a greedy/heuristic action selection approach that plans incrementally, one step at a time, to maximize the benefits based on the current state of the world. The long-term action selection results in a more principled solution that requires minimal human supervision, while better failure recovery is achieved by incorporating features of the greedy planning approach. All algorithms are fully implemented and tested on the Sony AIBO robots.
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Paper Nr: 434
Title:

AN ARTICULATED MODEL WITH A KALMAN FILTER FOR REAL TIME VISUAL TRACKING - Application to the Tracking of Pedestrians with a Monocular Camera

Authors:

Youssef Rouchdy

Abstract: This work presents a method for the visual tracking of articulated targets in image sequences in real time. Each part of the target object is considered as a region of interest and tracked by a parametric transformation. Prior geometric and dynamic informations about the target are introduced with a Kalman filter to guide the evolution of the tracking process of regions. An articulated model with two areas is proposed and applied to track pedestrians in the urban image sequences.
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