2014 22nd International Conference on Pattern Recognition

24-28 Aug. 2014

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  • [Front cover]

    Publication Year: 2014, Page(s): C4
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  • [Title page i]

    Publication Year: 2014, Page(s): i
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  • [Title page iii]

    Publication Year: 2014, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2014, Page(s): iv
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  • Table of contents

    Publication Year: 2014, Page(s):v - lviii
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  • Message from the General Chair

    Publication Year: 2014, Page(s): lvix
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  • Message from the Program Chairs

    Publication Year: 2014, Page(s): lx
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  • Message from the President of IAPR

    Publication Year: 2014, Page(s): lxi
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  • Invited Speakers

    Publication Year: 2014, Page(s):lxii - lxvi
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  • Contests, Tutorials, and Workshops

    Publication Year: 2014, Page(s):lxvii - lxviii
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  • Organizing Committee

    Publication Year: 2014, Page(s): lxix
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  • Track and Area Chairs

    Publication Year: 2014, Page(s):lxx - lxxiii
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  • Reviewers

    Publication Year: 2014, Page(s):lxxiv - lxxxii
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  • Statistical Optimization for Geometric Estimation: Minimization vs. Non-minimization

    Publication Year: 2014, Page(s):1 - 8
    Cited by:  Papers (2)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (323 KB) | HTML iconHTML

    We overview techniques for optimal geometric estimation from noisy observations for computer vision applications. We first describe techniques based on minimization of a given cost function: least squares (LS), maximum likelihood (ML), and Sampson error minimization. We then summarize techniques not based on minimization: one solves a given matrix equation. Different choices of the matrices in it ... View full abstract»

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  • Putting the Scientist in the Loop -- Accelerating Scientific Progress with Interactive Machine Learning

    Publication Year: 2014, Page(s):9 - 17
    Cited by:  Papers (3)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (232 KB) | HTML iconHTML

    Technology drives advances in science. Giving scientists access to more powerful tools for collecting and understanding data enables them to both ask and answer new kinds questions that were previously beyond their reach. Of these new tools at their disposal, machine learning offers the opportunity to understand and analyze data at unprecedented scales and levels of detail. The standard machine le... View full abstract»

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  • Discrete Visual Perception

    Publication Year: 2014, Page(s):18 - 25
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (170 KB) | HTML iconHTML

    Computational vision and biomedical image have made tremendous progress of the past decade. This is mostly due the development of efficient learning and inference algorithms which allow better, faster and richer modeling of visual perception tasks. Graph-based representations are among the most prominent tools to address such perception through the casting of perception as a graph optimization pro... View full abstract»

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  • Learning Features and Parts for Fine-Grained Recognition

    Publication Year: 2014, Page(s):26 - 33
    Cited by:  Papers (39)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (714 KB) | HTML iconHTML

    This paper addresses the problem of fine-grained recognition: recognizing subordinate categories such as bird species, car models, or dog breeds. We focus on two major challenges: learning expressive appearance descriptors and localizing discriminative parts. To this end, we propose an object representation that detects important parts and describes fine grained appearances. The part detectors are... View full abstract»

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  • Deep Metric Learning for Person Re-identification

    Publication Year: 2014, Page(s):34 - 39
    Cited by:  Papers (149)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (390 KB) | HTML iconHTML

    Various hand-crafted features and metric learning methods prevail in the field of person re-identification. Compared to these methods, this paper proposes a more general way that can learn a similarity metric from image pixels directly. By using a "siamese" deep neural network, the proposed method can jointly learn the color feature, texture feature and metric in a unified framework. The network h... View full abstract»

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  • Facial 3D Shape Estimation from Images for Visual Speech Animation

    Publication Year: 2014, Page(s):40 - 45
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (682 KB) | HTML iconHTML

    In this paper we describe the first version of our system for estimating 3D shape sequences from images of the frontal face. This approach is developed with 3D Visual Speech Animation (VSA) as the target application. In particular, the focus is on the usability of an existing state-of-the-art image-based VSA system and subsequent on-line estimation of the corresponding 3D facial shape sequence fro... View full abstract»

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  • Shape from Phase: An Integrated Level Set and Probability Density Shape Representation

    Publication Year: 2014, Page(s):46 - 51
    Cited by:  Papers (2)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (853 KB) | HTML iconHTML

    The past twenty years has seen the explosion of the "shape zoo": myriad shape representations, each with pros and cons. Of the varied denizens, distance transforms and density function shape representations have proven to be the most utile. Distance transforms inherit the numerous geometric advantages of implicit curve representations while density functions are unmatched in their approach to the ... View full abstract»

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  • LBO-Shape Densities: Efficient 3D Shape Retrieval Using Wavelet Density Estimation

    Publication Year: 2014, Page(s):52 - 57
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1135 KB) | HTML iconHTML

    Driven by desirable attributes such as topological characterization and invariance to isometric transformations, the use of the Laplace-Beltrami operator (LBO) and its associated spectrum have been widely adopted among the shape analysis community. Here we demonstrate a novel use of the LBO for shape matching and retrieval by estimating probability densities on its Eigen space, and subsequently us... View full abstract»

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  • Robust Point Set Matching under Variational Bayesian Framework

    Publication Year: 2014, Page(s):58 - 63
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (740 KB) | HTML iconHTML

    In this paper, we formulate a probabilistic point set matching problem under variational Bayesian framework and propose an iterative algorithm in which the posteriors of parameters are updated in sequence until a local optimum is reached. This variational Bayesian registration approach explicitly accounts for the matching uncertainty in terms of the parameters and is thus less prone to local optim... View full abstract»

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  • Spatially-Varying Image Warps for Scene Alignment

    Publication Year: 2014, Page(s):64 - 69
    Cited by:  Papers (2)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (2833 KB) | HTML iconHTML

    This paper proposes a method to align a set of images captured from multiple view points. Traditional methods using image warps parameterized by global transformations suffer from the problem of misalignment due to parallax effects induced by camera motions between images and depth variations of the scene. Our method parameterizes warps using mesh deformation and achieves spatially-varying transfo... View full abstract»

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  • Visualization of Hyperspectral Imaging Data Based on Manifold Alignment

    Publication Year: 2014, Page(s):70 - 75
    Cited by:  Papers (3)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1179 KB) | HTML iconHTML

    Tristimulus display of the abundant information contained in a hyper spectral image is a challenging task. Previous visualization approaches focused on preserving as much information as possible in the reduced spectral space, but ended up with displaying hyper spectral images as false color images, which contradicts with human experience and expectation. This paper proposes a new framework to tack... View full abstract»

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  • A Matrix Factorization Approach to Graph Compression

    Publication Year: 2014, Page(s):76 - 81
    Cited by:  Papers (2)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (218 KB) | HTML iconHTML

    We address the problem of encoding a graph of order n into a graph of order k <; n in a way to minimize reconstruction error. We characterize this encoding in terms of a particular factorization of the adjacency matrix of the original graph. The factorization is determined as the solution of a discrete optimization problem, which is for convenience relaxed into a continuous, but equivalent, one... View full abstract»

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