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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 6 • Date June 2008

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Displaying Results 1 - 23 of 23
  • [Front cover]

    Page(s): c1
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  • [Inside front cover]

    Page(s): c2
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  • Singular Points Detection Based on Zero-Pole Model in Fingerprint Images

    Page(s): 929 - 940
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4983 KB) |  | HTML iconHTML  

    An algorithm is proposed, which combines Zero-pole Model and Hough Transform (HT) to detect singular points. Orientation of singular points is defined on the basis of the Zero-pole Model, which can further explain the practicability of Zero-pole Model. Contrary to orientation field generation, detection of singular points is simplified to determine the parameters of the Zero-pole Model. HT uses rather global information of fingerprint images to detect singular points. This makes our algorithm more robust to noise than methods that only use local information. As the Zero-pole Model may have a little warp from actual fingerprint orientation field, Poincare index is used to make position adjustment in neighborhood of the detected candidate singular points. Experimental results show that our algorithm performs well and fast enough for real-time application in database NIST-4. View full abstract»

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  • Performance Evaluation and Benchmarking of Six-Page Segmentation Algorithms

    Page(s): 941 - 954
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3530 KB) |  | HTML iconHTML  

    Informative benchmarks are crucial for optimizing the page segmentation step of an OCR system, frequently the performance limiting step for overall OCR system performance. We show that current evaluation scores are insufficient for diagnosing specific errors in page segmentation and fail to identify some classes of serious segmentation errors altogether. This paper introduces a vectorial score that is sensitive to, and identifies, the most important classes of segmentation errors (over, under, and mis-segmentation) and what page components (lines, blocks, etc.) are affected. Unlike previous schemes, our evaluation method has a canonical representation of ground-truth data and guarantees pixel-accurate evaluation results for arbitrary region shapes. We present the results of evaluating widely used segmentation algorithms (x-y cut, smearing, whitespace analysis, constrained text-line finding, docstrum, and Voronoi) on the UW-III database and demonstrate that the new evaluation scheme permits the identification of several specific flaws in individual segmentation methods. View full abstract»

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  • A Hierarchical Compositional Model for Face Representation and Sketching

    Page(s): 955 - 969
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4656 KB) |  | HTML iconHTML  

    This paper presents a hierarchical-compositional model of human faces, as a three-layer AND-OR graph to account for the structural variabilities over multiple resolutions. In the AND-OR graph, an AND-node represents a decomposition of certain graphical structure, which expands to a set of OR-nodes with associated relations; an OR-node serves as a switch variable pointing to alternative AND-nodes. Faces are then represented hierarchically: The first layer treats each face as a whole, the second layer refines the local facial parts jointly as a set of individual templates, and the third layer further divides the face into 15 zones and models detail facial features such as eye corners, marks, or wrinkles. Transitions between the layers are realized by measuring the minimum description length (MDL) given the complexity of an input face image. Diverse face representations are formed by drawing from dictionaries of global faces, parts, and skin detail features. A sketch captures the most informative part of a face in a much more concise and potentially robust representation. However, generating good facial sketches is extremely challenging because of the rich facial details and large structural variations, especially in the high-resolution images. The representing power of our generative model is demonstrated by reconstructing high-resolution face images and generating the cartoon facial sketches. Our model is useful for a wide variety of applications, including recognition, nonphotorealisitc rendering, superresolution, and low-bit rate face coding. View full abstract»

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  • Tied Factor Analysis for Face Recognition across Large Pose Differences

    Page(s): 970 - 984
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2426 KB) |  | HTML iconHTML  

    Face recognition algorithms perform very unreliably when the pose of the probe face is different from the gallery face: typical feature vectors vary more with pose than with identity. We propose a generative model that creates a one-to-many mapping from an idealized "identity" space to the observed data space. In identity space, the representation for each individual does not vary with pose. We model the measured feature vector as being generated by a pose-contingent linear transformation of the identity variable in the presence of Gaussian noise. We term this model "tied" factor analysis. The choice of linear transformation (factors) depends on the pose, but the loadings are constant (tied) for a given individual. We use the EM algorithm to estimate the linear transformations and the noise parameters from training data. We propose a probabilistic distance metric that allows a full posterior over possible matches to be established. We introduce a novel feature extraction process and investigate recognition performance by using the FERET, XM2VTS, and PIE databases. Recognition performance compares favorably with contemporary approaches. View full abstract»

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  • Real-Time Computerized Annotation of Pictures

    Page(s): 985 - 1002
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3722 KB) |  | HTML iconHTML  

    Developing effective methods for automated annotation of digital pictures continues to challenge computer scientists. The capability of annotating pictures by computers can lead to breakthroughs in a wide range of applications, including Web image search, online picture-sharing communities, and scientific experiments. In this work, the authors developed new optimization and estimation techniques to address two fundamental problems in machine learning. These new techniques serve as the basis for the automatic linguistic indexing of pictures - real time (ALIPR) system of fully automatic and high-speed annotation for online pictures. In particular, the D2-clustering method, in the same spirit as K-Means for vectors, is developed to group objects represented by bags of weighted vectors. Moreover, a generalized mixture modeling technique (kernel smoothing as a special case) for nonvector data is developed using the novel concept of hypothetical local mapping (HLM). ALIPR has been tested by thousands of pictures from an Internet photo-sharing site, unrelated to the source of those pictures used in the training process. Its performance has also been studied at an online demonstration site, where arbitrary users provide pictures of their choices and indicate the correctness of each annotation word. The experimental results show that a single computer processor can suggest annotation terms in real time and with good accuracy. View full abstract»

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  • Geometry-Based Image Retrieval in Binary Image Databases

    Page(s): 1003 - 1013
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (867 KB) |  | HTML iconHTML  

    In this paper, a geometry-based image retrieval system is developed for multiobject images. We model both shape and topology of image objects using a structured representation called curvature tree (CT). The hierarchy of the CT reflects the inclusion relationships between the image objects. To facilitate shape-based matching, triangle-area representation (TAR) of each object is stored at the corresponding node in the CT. The similarity between two multiobject images is measured based on the maximum similarity subtree isomorphism (MSSI) between their CTs. For this purpose, we adopt a recursive algorithm to solve the MSSI problem and a very effective dynamic programming algorithm to measure the similarity between the attributed nodes. Our matching scheme agrees with many recent findings in psychology about the human perception of multiobject images. Experiments on a database of 13,500 real and synthesized medical images and the MPEG-7 CE-1 database of 1,400 shape images have shown the effectiveness of the proposed method. View full abstract»

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  • Edge-Preserving Filtering of Images with Low Photon Counts

    Page(s): 1014 - 1027
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4185 KB) |  | HTML iconHTML  

    Edge-preserving filters such as local M-smoothers or bilateral filtering are usually designed for Gaussian noise. This paper investigates how these filters can be adapted in order to efficiently deal with Poissonian noise. In addition, the issue of photometry invariance is addressed by changing the way filter coefficients are normalized. The proposed normalization is additive, instead of being multiplicative, and leads to a strong connection with anisotropic diffusion. Experiments show that ensuring the photometry invariance leads to comparable denoising performances in terms of the root mean square error computed on the signal. View full abstract»

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  • Multilayered 3D LiDAR Image Construction Using Spatial Models in a Bayesian Framework

    Page(s): 1028 - 1040
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1954 KB) |  | HTML iconHTML  

    Standard 3D imaging systems process only a single return at each pixel from an assumed single opaque surface. However, there are situations when the laser return consists of multiple peaks due to the footprint of the beam impinging on a target with surfaces distributed in depth or with semitransparent surfaces. If all these returns are processed, a more informative multilayered 3D image is created. We propose a unified theory of pixel processing for Lidar data using a Bayesian approach that incorporates spatial constraints through a Markov Random Field with a Potts prior model. This allows us to model uncertainty about the underlying spatial process. To palliate some inherent deficiencies of this prior model, we also introduce two proposal distributions, one based on spatial mode jumping and the other on a spatial birth/death process. The different parameters of the several returns are estimated using reversible-jump Markov chain Monte Carlo (RJMCMC) techniques in combination with an adaptive strategy of delayed rejection to improve the estimates of the parameters. View full abstract»

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  • Subclass Problem-Dependent Design for Error-Correcting Output Codes

    Page(s): 1041 - 1054
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5993 KB) |  | HTML iconHTML  

    A common way to model multiclass classification problems is by means of Error-Correcting Output Codes (ECOCs). Given a multiclass problem, the ECOC technique designs a code word for each class, where each position of the code identifies the membership of the class for a given binary problem. A classification decision is obtained by assigning the label of the class with the closest code. One of the main requirements of the ECOC design is that the base classifier is capable of splitting each subgroup of classes from each binary problem. However, we cannot guarantee that a linear classifier model convex regions. Furthermore, nonlinear classifiers also fail to manage some type of surfaces. In this paper, we present a novel strategy to model multiclass classification problems using subclass information in the ECOC framework. Complex problems are solved by splitting the original set of classes into subclasses and embedding the binary problems in a problem-dependent ECOC design. Experimental results show that the proposed splitting procedure yields a better performance when the class overlap or the distribution of the training objects conceal the decision boundaries for the base classifier. The results are even more significant when one has a sufficiently large training size. View full abstract»

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  • Triplet Markov Fields for the Classification of Complex Structure Data

    Page(s): 1055 - 1067
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2850 KB) |  | HTML iconHTML  

    We address the issue of classifying complex data. We focus on three main sources of complexity, namely, the high dimensionality of the observed data, the dependencies between these observations, and the general nature of the noise model underlying their distribution. We investigate the recent Triplet Markov Fields and propose new models in this class designed for such data and in particular allowing very general noise models. In addition, our models can handle the inclusion of a learning step in a consistent way so that they can be used in a supervised framework. One advantage of our models is that whatever the initial complexity of the noise model, parameter estimation can be carried out using state-of-the-art Bayesian clustering techniques under the usual simplifying assumptions. As generative models, they can be seen as an alternative, in the supervised case, to discriminative Conditional Random Fields. Identifiability issues underlying the models in the nonsupervised case are discussed while the models performance is illustrated on simulated and real data, exhibiting the mentioned various sources of complexity. View full abstract»

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  • A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors

    Page(s): 1068 - 1080
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3653 KB) |  | HTML iconHTML  

    Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation. It has been known for decades that such problems can be elegantly expressed as Markov random fields, yet the resulting energy minimization problems have been widely viewed as intractable. Algorithms such as graph cuts and loopy belief propagation (LBP) have proven to be very powerful: For example, such methods form the basis for almost all the top-performing stereo methods. However, the trade-offs among different energy minimization algorithms are still not well understood. In this paper, we describe a set of energy minimization benchmarks and use them to compare the solution quality and runtime of several common energy minimization algorithms. We investigate three promising methods-graph cuts, LBP, and tree-reweighted message passing-in addition to the well-known older iterated conditional mode (ICM) algorithm. Our benchmark problems are drawn from published energy functions used for stereo, image stitching, interactive segmentation, and denoising. We also provide a general-purpose software interface that allows vision researchers to easily switch between optimization methods. The benchmarks, code, images, and results are available at http://vision.middlebury.edu/MRF/. View full abstract»

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  • Optical Flow and Advection on 2-Riemannian Manifolds: A Common Framework

    Page(s): 1081 - 1092
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    Dynamic pattern analysis and motion extraction can be efficiently addressed using optical flow techniques. This paper presents a generalization of these questions to nonflat surfaces, where optical flow is tackled through the problem of evolution processes on non-euclidean domains. The classical equations of optical flow in the euclidean case are transposed to the theoretical framework of differential geometry. We adopt this formulation for the regularized optical flow problem, prove its mathematical well posedness, and combine it with the advection equation. The optical flow and advection problems are dual: A motion field may be retrieved from some scalar evolution using optical flow; conversely, a scalar field may be deduced from a velocity field using advection. These principles are illustrated with qualitative and quantitative evaluations from numerical simulations bridging both approaches. The proof-of-concept is further demonstrated with preliminary results from time-resolved functional brain imaging data, where organized propagations of cortical activation patterns are evidenced using our approach. View full abstract»

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  • Geometric Observers for Dynamically Evolving Curves

    Page(s): 1093 - 1108
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2812 KB) |  | HTML iconHTML  

    This paper proposes a deterministic observer design for visual tracking based on nonparametric implicit (level-set) curve descriptions. The observer is continuous discrete with continuous-time system dynamics and discrete-time measurements. Its state- space consists of an estimated curve position augmented by additional states (e.g., velocities) associated with every point on the estimated curve. Multiple simulation models are proposed for state prediction. Measurements are performed through standard static segmentation algorithms and optical-flow computations. Special emphasis is given to the geometric formulation of the overall dynamical system. The discrete-time measurements lead to the problem of geometric curve interpolation and the discrete-time filtering of quantities propagated along with the estimated curve. Interpolation and filtering are intimately linked to the correspondence problem between curves. Correspondences are established by a Laplace-equation approach. The proposed scheme is implemented completely implicitly (by Eulerian numerical solutions of transport equations) and thus naturally allows for topological changes and subpixel accuracy on the computational grid. View full abstract»

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  • Glove-Based Approach to Online Signature Verification

    Page(s): 1109 - 1113
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1060 KB) |  | HTML iconHTML  

    Utilizing the multiple degrees of freedom offered by the data glove for each finger and the hand, a novel online signature verification system using the singular value decomposition (SVD) numerical tool for signature classification and verification is presented. The proposed technique is based on the Singular value decomposition in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, so the effective dimensionality of A can be reduced. Having modeled the data glove signature through its r-principal subspace, signature authentication is performed by finding the angles between the different subspaces. A demonstration of the data glove is presented as an effective high- bandwidth data entry device for signature verification. This SVD-based signature verification technique is tested and its performance is shown to be able to produce equal error rate (EER) of less than 2.37 percent. View full abstract»

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  • On Distributional Assumptions and Whitened Cosine Similarities

    Page(s): 1114 - 1115
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    Recently, an interpretation of the whitened cosine similarity measure as a Bayes decision rule was proposed [1]. This communication makes the observation that some of the distributional assumptions made to derive this measure are very restrictive and, considered simultaneously even inconsistent. View full abstract»

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  • Clarification of Assumptions in the Relationship between the Bayes Decision Rule and the Whitened Cosine Similarity Measure

    Page(s): 1116 - 1117
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (71 KB) |  | HTML iconHTML  

    This paper first clarifies Assumption 3 (which misses a constant) and Assumption 4 (where the whitened pattern vectors represent the whitened means) in the paper ";The Bayes Decision Rule Induced Similarity Measures"; (IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1086- 1090, June 2007) and then provides examples to show that the assumptions after the clarification are consistent. View full abstract»

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  • Correction to "Gaussian Process Dynamical Models for Human Motion" [Feb 08 283-298]

    Page(s): 1118
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    In the above titled paper (ibid., vol. 30, no. 2, pp. 283-298, Feb 08), two figures were misprinted. The correct figures are presented here. View full abstract»

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  • Call for papers for Special Section on Shape Analysis and Its Applications in Image Understanding

    Page(s): 1119
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    Freely Available from IEEE
  • Join the IEEE Computer Society [advertisement]

    Page(s): 1120
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    Freely Available from IEEE
  • TPAMI Information for authors

    Page(s): c3
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    Freely Available from IEEE
  • [Back cover]

    Page(s): c4
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