IEEE Transactions on Pattern Analysis and Machine Intelligence

Issue 5 • May 2002

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Displaying Results 1 - 12 of 12
  • Editorial - state of the transactions

    Publication Year: 2002, Page(s):577 - 578
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    Freely Available from IEEE
  • Estimation of occlusion and dense motion fields in a bidirectional Bayesian framework

    Publication Year: 2002, Page(s):712 - 718
    Cited by:  Papers (27)  |  Patents (27)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2144 KB) | HTML iconHTML

    This paper presents new MRF (Markov random field) models in a bidirectional Bayesian framework for accurate motion and occlusion field estimation. With careful selection of the five free parameters required by the models, good experimental results have been obtained. The resultant computational speed is also 5.5 times faster compared with the conventional "iterated conditional mode" relaxation usi... View full abstract»

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  • Orthogonal distance fitting of implicit curves and surfaces

    Publication Year: 2002, Page(s):620 - 638
    Cited by:  Papers (44)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1659 KB) | HTML iconHTML

    Dimensional model fitting finds its applications in various fields of science and engineering and is a relevant subject in computer/machine vision and coordinate metrology. In this paper, we present two new fitting algorithms, distance-based and coordinate-based algorithm, for implicit surfaces and plane curves, which minimize the square sum of the orthogonal error distances between the model feat... View full abstract»

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  • Learning gender with support faces

    Publication Year: 2002, Page(s):707 - 711
    Cited by:  Papers (271)  |  Patents (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (697 KB) | HTML iconHTML

    Nonlinear support vector machines (SVMs) are investigated for appearance-based gender classification with low-resolution "thumbnail" faces processed from 1,755 images from the FERET (FacE REcognition Technology) face database. The performance of SVMs (3.4% error) is shown to be superior to traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, nearest-neighbor) as well as ... View full abstract»

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  • Mean shift: a robust approach toward feature space analysis

    Publication Year: 2002, Page(s):603 - 619
    Cited by:  Papers (4394)  |  Patents (111)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3254 KB) | HTML iconHTML

    A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density fun... View full abstract»

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  • Face detection in color images

    Publication Year: 2002, Page(s):696 - 706
    Cited by:  Papers (819)  |  Patents (30)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3245 KB) | HTML iconHTML

    Human face detection plays an important role in applications such as video surveillance, human computer interface, face recognition, and face image database management. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds. Based on a novel lighting compensation technique and a nonlinear color transformation, our method... View full abstract»

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  • A new pattern representation scheme using data compression

    Publication Year: 2002, Page(s):579 - 590
    Cited by:  Papers (34)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3407 KB) | HTML iconHTML

    We propose the PRDC (Pattern Representation based on Data Compression) scheme for media data analysis. PRDC is composed of two parts: an encoder that translates input data into text and a set of text compressors to generate a compression-ratio vector (CV). The CV is used as a feature of the input data. By preparing a set of media-specific encoders, PRDC becomes widely applicable. Analysis tasks - ... View full abstract»

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  • A curve fitting problem and its application in modeling objects in monocular image sequences

    Publication Year: 2002, Page(s):674 - 686
    Cited by:  Papers (3)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1566 KB) | HTML iconHTML

    Presents a solution to a particular curve (surface) fitting problem and demonstrate its application in modeling objects from monocular image sequences. The curve-fitting algorithm is based on a modified nonparametric regression method, which forms the core contribution of this work. This method is far more effective compared to standard estimation techniques, such as the maximum likelihood estimat... View full abstract»

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  • Hierarchical GTM: constructing localized nonlinear projection manifolds in a principled way

    Publication Year: 2002, Page(s):639 - 656
    Cited by:  Papers (30)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3449 KB) | HTML iconHTML

    It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets and, therefore, a hierarchical visualization system is desirable. In this paper, we extend an existing locally linear hierarchical visualization system PhiVis in several directions: 1) We allow for nonlinear projection manifolds. The basic buildin... View full abstract»

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  • Upper bounds for error rates of linear combinations of classifiers

    Publication Year: 2002, Page(s):591 - 602
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2867 KB) | HTML iconHTML

    A useful notion of weak dependence between many classifiers constructed with the same training data is introduced. It is shown that if both this weak dependence is low and the expected margins are large, then decision rules based on linear combinations of these classifiers can achieve error rates that decrease exponentially fast. Empirical results with randomized trees and trees constructed via bo... View full abstract»

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  • Paracatadioptric camera calibration

    Publication Year: 2002, Page(s):687 - 695
    Cited by:  Papers (101)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (775 KB) | HTML iconHTML

    Catadioptric sensors refer to the combination of lens-based devices and reflective surfaces. These systems are useful because they may have a field of view which is greater than hemispherical, providing the ability to simultaneously view in any direction. Configurations which have a unique effective viewpoint are of primary interest, among these is the case where the reflective surface is a parabo... View full abstract»

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  • Image segmentation by data-driven Markov chain Monte Carlo

    Publication Year: 2002, Page(s):657 - 673
    Cited by:  Papers (244)  |  Patents (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2623 KB) | HTML iconHTML

    This paper presents a computational paradigm called Data-Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian statistical framework. The paper contributes to image segmentation in four aspects. First, it designs efficient and well-balanced Markov Chain dynamics to explore the complex solution space and, thus, achieves a nearly global optimal solution independent of initi... View full abstract»

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Aims & Scope

The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is published monthly. Its editorial board strives to present most important research results in areas within TPAMI's scope.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Sven Dickinson
University of Toronto
e-mail: sven@cs.toronto.edu