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IEEE Transactions on Pattern Analysis and Machine Intelligence

Issue 3 • Date March 2006

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Displaying Results 1 - 21 of 21
  • [Front cover]

    Publication Year: 2006, Page(s): c1
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  • [Inside front cover]

    Publication Year: 2006, Page(s): c2
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  • Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling

    Publication Year: 2006, Page(s):337 - 350
    Cited by:  Papers (73)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3157 KB) | HTML iconHTML

    Linear subspace methods that provide sufficient reconstruction of the data, such as PCA, offer an efficient way of dealing with missing pixels, outliers, and occlusions that often appear in the visual data. Discriminative methods, such as LDA, which, on the other hand, are better suited for classification tasks, are highly sensitive to corrupted data. We present a theoretical framework for achievi... View full abstract»

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  • Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics

    Publication Year: 2006, Page(s):351 - 363
    Cited by:  Papers (123)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1853 KB) | HTML iconHTML

    In this paper, we propose two novel methods for face recognition under arbitrary unknown lighting by using spherical harmonics illumination representation, which require only one training image per subject and no 3D shape information. Our methods are based on the result which demonstrated that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can ... View full abstract»

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  • Unsupervised, information-theoretic, adaptive image filtering for image restoration

    Publication Year: 2006, Page(s):364 - 376
    Cited by:  Papers (111)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1858 KB) | HTML iconHTML

    Image restoration is an important and widely studied problem in computer vision and image processing. Various image filtering strategies have been effective, but invariably make strong assumptions about the properties of the signal and/or degradation. Hence, these methods lack the generality to be easily applied to new applications or diverse image collections. This paper describes a novel unsuper... View full abstract»

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  • Incremental nonlinear dimensionality reduction by manifold learning

    Publication Year: 2006, Page(s):377 - 391
    Cited by:  Papers (102)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (6235 KB) | HTML iconHTML

    Understanding the structure of multidimensional patterns, especially in unsupervised cases, is of fundamental importance in data mining, pattern recognition, and machine learning. Several algorithms have been proposed to analyze the structure of high-dimensional data based on the notion of manifold learning. These algorithms have been used to extract the intrinsic characteristics of different type... View full abstract»

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  • Ordering and finding the best of K > 2 supervised learning algorithms

    Publication Year: 2006, Page(s):392 - 402
    Cited by:  Papers (7)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2439 KB) | HTML iconHTML

    Given a data set and a number of supervised learning algorithms, we would like to find the algorithm with the smallest expected error. Existing pairwise tests allow a comparison of two algorithms only; range tests and ANOVA check whether multiple algorithms have the same expected error and cannot be used for finding the smallest. We propose a methodology, the multitest algorithm, whereby we order ... View full abstract»

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  • Nonsmooth nonnegative matrix factorization (nsNMF)

    Publication Year: 2006, Page(s):403 - 415
    Cited by:  Papers (133)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3964 KB) | HTML iconHTML

    We propose a novel nonnegative matrix factorization model that aims at finding localized, part-based, representations of nonnegative multivariate data items. Unlike the classical nonnegative matrix factorization (NMF) technique, this new model, denoted "nonsmooth nonnegative matrix factorization" (nsNMF), corresponds to the optimization of an unambiguous cost function designed to explicitly repres... View full abstract»

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  • Generic object recognition with boosting

    Publication Year: 2006, Page(s):416 - 431
    Cited by:  Papers (133)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (6053 KB) | HTML iconHTML

    This paper explores the power and the limitations of weakly supervised categorization. We present a complete framework that starts with the extraction of various local regions of either discontinuity or homogeneity. A variety of local descriptors can be applied to form a set of feature vectors for each local region. Boosting is used to learn a subset of such feature vectors (weak hypotheses) and t... View full abstract»

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  • Real-time range acquisition by adaptive structured light

    Publication Year: 2006, Page(s):432 - 445
    Cited by:  Papers (103)  |  Patents (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (5130 KB) | HTML iconHTML

    The goal of this paper is to provide a "self-adaptive" system for real-time range acquisition. Reconstructions are based on a single frame structured light illumination. Instead of using generic, static coding that is supposed to work under all circumstances, system adaptation is proposed. This occurs on-the-fly and renders the system more robust against instant scene variability and creates suita... View full abstract»

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  • Relief texture from specularities

    Publication Year: 2006, Page(s):446 - 457
    Cited by:  Papers (16)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4247 KB) | HTML iconHTML

    In vision and graphics, advanced object models require not only 3D shape, but also surface detail. While several scanning devices exist to capture the global shape of an object, few methods concentrate on capturing the fine-scale detail. Fine-scale surface geometry (relief texture), such as surface markings, roughness, and imprints, is essential in highly realistic rendering and accurate predictio... View full abstract»

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  • Hidden Markov models combining discrete symbols and continuous attributes in handwriting recognition

    Publication Year: 2006, Page(s):458 - 462
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (957 KB) | HTML iconHTML

    Prior arts in handwritten word recognition model either discrete features or continuous features, but not both. This paper combines discrete symbols and continuous attributes into structural handwriting features and model, them by transition-emitting and state-emitting hidden Markov models. The models are rigorously defined and experiments have proven their effectiveness. View full abstract»

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  • Context-based segmentation of image sequences

    Publication Year: 2006, Page(s):463 - 468
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1125 KB) | HTML iconHTML

    We describe an algorithm for context-based segmentation of visual data. New frames in an image sequence (video) are segmented based on the prior segmentation of earlier frames in the sequence. The segmentation is performed by adapting a probabilistic model learned on previous frames, according to the content of the new frame. We utilize the maximum a posteriori version of the EM algorithm to segme... View full abstract»

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  • Isoperimetric graph partitioning for image segmentation

    Publication Year: 2006, Page(s):469 - 475
    Cited by:  Papers (92)  |  Patents (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1867 KB) | HTML iconHTML

    Spectral graph partitioning provides a powerful approach to image segmentation. We introduce an alternate idea that finds partitions with a small isoperimetric constant, requiring solution to a linear system rather than an eigenvector problem. This approach produces the high quality segmentations of spectral methods, but with improved speed and stability. View full abstract»

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  • Estimation of high-density regions using one-class neighbor machines

    Publication Year: 2006, Page(s):476 - 480
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (588 KB) | HTML iconHTML

    In this paper, we investigate the problem of estimating high-density regions from univariate or multivariate data samples. We estimate minimum volume sets, whose probability is specified in advance, known in the literature as density contour clusters. This problem is strongly related to one-class support vector machines (OCSVM). We propose a new method to solve this problem, the one-class neighbor... View full abstract»

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  • Minimum reliable scale selection in 3D

    Publication Year: 2006, Page(s):481 - 487
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1146 KB) | HTML iconHTML

    Multiscale analysis is often required in image processing applications because image features are optimally detected at different levels of resolution. With the advance of high-resolution 3D imaging, the extension of multiscale analysis to higher dimensions is necessary. This paper extends an existing 2D scale selection method, known as the minimum reliable scale, to 3D volumetric images. The meth... View full abstract»

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  • Mumford and Shah functional: VLSI analysis and implementation

    Publication Year: 2006, Page(s):487 - 494
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2908 KB) | HTML iconHTML

    This paper describes the analysis of the Mumford and Shah functional from the implementation point of view. Our goal is to show results in terms of complexity for real-time applications, such as motion estimation based on segmentation techniques, of the Mumford and Shah functional. Moreover, the sensitivity to finite precision representation is addressed, a fast VLSI architecture is described, and... View full abstract»

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  • [Advertisement]

    Publication Year: 2006, Page(s): 495
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  • [Advertisement]

    Publication Year: 2006, Page(s): 496
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  • TPAMI Information for authors

    Publication Year: 2006, Page(s): c3
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  • [Back cover]

    Publication Year: 2006, Page(s): c4
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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
David A. Forsyth
University of Illinois
e-mail: daf@illinois.edu