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Pattern Recognition (ICPR), 2010 20th International Conference on

Date 23-26 Aug. 2010

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  • [Front cover]

    Page(s): C1
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  • [Title page i]

    Page(s): i
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  • [Title page iii]

    Page(s): iii
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  • [Copyright notice]

    Page(s): iv
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  • Table of contents

    Page(s): v - lxxvii
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  • Message from General Chair

    Page(s): lxxviii - lxxix
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  • Message from Technical Program Chairs

    Page(s): lxxx
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  • Organizing Committee

    Page(s): lxxxi - lxxxii
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  • ICPR 2010 Track Co-chairs

    Page(s): lxxxiii - lxxxiv
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  • Reviewers

    Page(s): lxxxv - xci
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  • Minimizing Geometric Distance by Iterative Linear Optimization

    Page(s): 1 - 4
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (500 KB) |  | HTML iconHTML  

    This paper proposes an algorithm that solves planar homography by iterative linear optimization. We iteratively employ direct linear transformation (DLT) algorithm to robustly estimate the homography induced by a given set of point correspondences under perspective transformation. By simple on-the-fly homogeneous coordinate adjustment we progressively minimize the difference between the algebraic error and the geometric error. When the difference is sufficiently close to zero, the geometric error is equivalently minimized and the homography is reliably solved. Backward covariance propagation is employed to do error analysis. The experiments prove that the algorithm is able to find global minimum despite erroneous initialization. It gives very precise estimate at low computational cost and greatly outperforms existing techniques. View full abstract»

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  • Hyper Least Squares and Its Applications

    Page(s): 5 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (518 KB) |  | HTML iconHTML  

    We present a new form of least squares (LS), called "hyper LS", for geometric problems that frequently appear in computer vision applications. Doing rigorous error analysis, we maximize the accuracy by introducing a normalization that eliminates statistical bias up to second order noise terms. Our method yields a solution comparable to maximum likelihood (ML) without iterations, even in large noise situations where ML computation fails. View full abstract»

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  • Integrating a Discrete Motion Model into GMM Based Background Subtraction

    Page(s): 9 - 12
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (604 KB) |  | HTML iconHTML  

    GMM based algorithms have become the de facto standard for background subtraction in video sequences, mainly because of their ability to track multiple background distributions, which allows them to handle complex scenes including moving trees, flags moving in the wind etc. However, it is not always easy to determine which distributions of the mixture belong to the background and which distributions belong to the foreground, which disturbs the results of the labeling process for each pixel. In this work we tackle this problem by taking the labeling decision together for all pixels of several consecutive frames minimizing a global energy function taking into account spatial and temporal relationships. A discrete approximative optical-flow like motion model is integrated into the energy function and solved with Ishikawa's convex graph cuts algorithm. View full abstract»

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  • Saliency Based on Multi-scale Ratio of Dissimilarity

    Page(s): 13 - 16
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (670 KB) |  | HTML iconHTML  

    Recently, many vision applications tend to utilize saliency maps derived from input images to guide them to focus on processing salient regions in images. In this paper, we propose a simple and effective method to quantify the saliency for each pixel in images. Specially, we define the saliency for a pixel in a ratio form, where the numerator measures the number of dissimilar pixels in its center-surround and the denominator measures the total number of pixels in its center-surround. The final saliency is obtained by combining these ratios of dissimilarity over multiple scales. For images, the saliency map generated by our method not only has a high quality in resolution also looks more reasonable. Finally, we apply our saliency map to extract the salient regions in images, and compare the performance with some state-of-the-art methods over an established ground-truth which contains 1000 images. View full abstract»

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  • Online Principal Background Selection for Video Synopsis

    Page(s): 17 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (556 KB) |  | HTML iconHTML  

    Video synopsis provides a means for fast browsing of activities in video. Principal background selection (PBS) is an important step in video synopsis. Existing methods make PBS in an offline way and at a high memory cost. In this paper we propose a novel background selection method, ``online principal background selection'' (OPBS). The OPBS selects n principal backgrounds from N backgrounds in an online fashion with a low memory cost, making it possible to build an efficient online video synopsis system. Another advantage is that, with OPBS, the selected backgrounds are related to not only background changes over time but also video activities. Experimental results demonstrate the advantages of the proposed OPBS. View full abstract»

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  • Large Margin Classifier Based on Affine Hulls

    Page(s): 21 - 24
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (470 KB) |  | HTML iconHTML  

    This paper introduces a geometrically inspired large-margin classifier that can be a better alternative to the Support Vector Machines (SVMs) for the classification problems with limited number of training samples. In contrast to the SVM classifier, we approximate classes with affine hulls of their class samples rather than convex hulls, which may be unrealistically tight in high-dimensional spaces. To find the best separating hyperplane between any pair of classes approximated with the affine hulls, we first compute the closest points on the affine hulls and connect these two points with a line segment. The optimal separating hyperplane is chosen to be the hyperplane that is orthogonal to the line segment and bisects the line. To allow soft margin solutions, we first reduce affine hulls in order to alleviate the effects of outliers and then search for the best separating hyperplane between these reduced models. Multi-class classification problems are dealt with constructing and combining several binary classifiers as in SVM. The experiments on several databases show that the proposed method compares favorably with the SVM classifier. View full abstract»

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  • 2D Shape Recognition Using Information Theoretic Kernels

    Page(s): 25 - 28
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (798 KB) |  | HTML iconHTML  

    In this paper, a novel approach for contour based 2D shape recognition is proposed, using a class of information theoretic kernels recently introduced. This kind of kernels, based on a non-extensive generalization of the classical Shannon information theory, are defined on probability measures. In the proposed approach, chain code representations are first extracted from the contours; then n-gram statistics are computed and used as input to the information theoretic kernels. We tested different versions of such kernels, using support vector machine and nearest neighbor classifiers. An experimental evaluation on the Chicken pieces dataset shows that the proposed approach significantly outperforms the current state-of-the-art methods. View full abstract»

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  • Time Series Classification Using Support Vector Machine with Gaussian Elastic Metric Kernel

    Page(s): 29 - 32
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (507 KB) |  | HTML iconHTML  

    Motivated by the great success of dynamic time warping (DTW) in time series matching, Gaussian DTW kernel had been developed for support vector machine (SVM)-based time series classification. Counter-examples, however, had been subsequently reported that Gaussian DTW kernel usually cannot outperform Gaussian RBF kernel in the SVM framework. In this paper, by extending the Gaussian RBF kernel, we propose one novel class of Gaussian elastic metric kernel (GEMK), and present two examples of GEMK: Gaussian time warp edit distance (GTWED) kernel and Gaussian edit distance with real penalty (GERP) kernel. Experimental results on UCR time series data sets show that, in terms of classification accuracy, SVM with GEMK is much superior to SVM with Gaussian RBF kernel and Gaussian DTW kernel, and the state-of-the-art similarity measure methods. View full abstract»

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  • Multiplicative Update Rules for Multilinear Support Tensor Machines

    Page(s): 33 - 36
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (624 KB) |  | HTML iconHTML  

    In this paper, we formulate the Multilinear Support Tensor Machines (MSTMs) problem in a similar to the Non-negative Matrix Factorization (NMF) algorithm way. A novel set of simple and robust multiplicative update rules are proposed in order to find the multilinear classifier. Updates rules are provided for both hard and soft margin MSTMs and the existence of a bias term is also investigated. We present results on standard gait and action datasets and report faster convergence of equivalent classification performance in comparison to standard MSTMs. View full abstract»

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  • Support Vectors Selection for Supervised Learning Using an Ensemble Approach

    Page(s): 37 - 40
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (565 KB) |  | HTML iconHTML  

    Support Vector Machines (SVMs) are popular for pattern classification. However, training a SVM requires large memory and high processing time, especially for large datasets, which limits their applications. To speed up their training, we present a new efficient support vector selection method based on ensemble margin, a key concept in ensemble classifiers. This algorithm exploits a new version of the margin of an ensemble-based classification and selects the smallest margin instances as support vectors. Our experimental results show that our method reduces training set size significantly without degrading the performance of the resulting SVMs classifiers. View full abstract»

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  • Estimating Apparent Motion on Satellite Acquisitions with a Physical Dynamic Model

    Page(s): 41 - 44
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3846 KB) |  | HTML iconHTML  

    The paper presents a motion estimation method based on data assimilation in a dynamic model, named Image Model, expressing the physical evolution of a quantity observed on the images. The application concerns the retrieval of apparent surface velocity from a sequence of satellite data, acquired over the ocean. The Image Model includes a shallow-water approximation for the dynamics of the velocity field (the evolution of the two components of motion are linked by the water layer thickness) and a transport equation for the image field. For retrieving the surface velocity, a sequence of Sea Surface Temperature (SST) acquisitions is assimilated in the Image Model with a 4D-Var method. This is based on the minimization of a cost function including the discrepancy between model outputs and SST data and a regularization term. Several types of regularization norms have been studied. Results are discussed to analyze the impact of the different components of the assimilation system. View full abstract»

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  • Multiple View Geometries for Mirrors and Cameras

    Page(s): 45 - 48
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (623 KB) |  | HTML iconHTML  

    In this paper, we analyze the multiple view geometry for a camera and mirrors, and propose a method for computing the geometry of the camera and mirrors accurately from fewer corresponding points than the existing methods. The geometry between a camera and mirrors can be described as the multiple view geometry for a real camera and virtual cameras. We show that very strong constraints on geometries can be obtained in addition to the ordinary multilinear constraints. By using these constraints, we can estimate multiple view geometry more accurately from fewer corresponding points than usual. The experimental results show the efficiency of the proposed method. View full abstract»

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  • Perspective Reconstruction and Camera Auto-Calibration as Rectangular Polynomial Eigenvalue Problem

    Page(s): 49 - 52
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1460 KB) |  | HTML iconHTML  

    Motion-based 3D reconstruction (SfM) with missing data has been a challenging computer vision task since the late 90s. Under perspective camera model, one of the most difficult problems is camera auto-calibration which means determining the intrinsic camera parameters without using any known calibration object or assuming special properties of the scene. This paper presents a novel algorithm to perform camera auto-calibration from multiple images and dealing with the missing data problem. The method supposes semi-calibrated cameras (every intrinsic camera parameter except for the focal length is considered to be known) and constant focal length over all the images. The solution requires at least one image pair having at least eight common measured points. Tests verified that the algorithm is numerically stable and produces accurate results both on synthetic and real test sequences. View full abstract»

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  • Multi-camera Platform Calibration Using Multi-linear Constraints

    Page(s): 53 - 56
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1407 KB) |  | HTML iconHTML  

    We present a novel calibration method for multi-camera platforms, based on multi-linear constraints. The calibration method can recover the relative orientation between the different cameras on the platform, even when there are no corresponding feature points between the cameras, i.e. there are no overlaps between the cameras. It is shown that two translational motions in different directions are sufficient to linearly recover the rotational part of the relative orientation. Then two general motions, including both translation and rotation, are sufficient to linearly recover the translational part of the relative orientation. However, as a consequence of the speed-scale ambiguity the absolute scale of the translational part can not be determined if no prior information about the motions are known, e.g. from dead reckoning. It is shown that in case of planar motion, the vertical component of the translational part can not be determined. However, if at least one feature point can be seen in two different cameras, this vertical component can also be estimated. Finally, the performance of the proposed method is shown in simulated experiments. View full abstract»

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  • A Game-Theoretic Approach to Robust Selection of Multi-view Point Correspondence

    Page(s): 57 - 60
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1693 KB) |  | HTML iconHTML  

    In this paper we introduce a robust matching technique that allows very accurate selection of corresponding feature points from multiple views. Robustness is achieved by enforcing global geometric consistency at an early stage of the matching process, without the need of subsequent verification through reprojection. The global consistency is reduced to a pairwise compatibility making use of the size and orientation information provided by common feature descriptors, thus projecting what is a high-order compatibility problem into a pairwise setting. Then a game-theoretic approach is used to select a maximally consistent set of candidate matches, where highly compatible matches are enforced while incompatible correspondences are driven to extinction. View full abstract»

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