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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 3 • Date March 2006

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

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

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  • Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling

    Page(s): 337 - 350
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 achieving the best of both types of methods: an approach that combines the discrimination power of discriminative methods with the reconstruction property of reconstructive methods which enables one to work on subsets of pixels in images to efficiently detect and reject the outliers. The proposed approach is therefore capable of robust classification with a high-breakdown point. We also show that subspace methods, such as CCA, which are used for solving regression tasks, can be treated in a similar manner. The theoretical results are demonstrated on several computer vision tasks showing that the proposed approach significantly outperforms the standard discriminative methods in the case of missing pixels and images containing occlusions and outliers. View full abstract»

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

    Page(s): 351 - 363
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 be approximated accurately by a low-dimensional linear subspace. We provide two methods to estimate the spherical harmonic basis images spanning this space from just one image. Our first method builds the statistical model based on a collection of 2D basis images. We demonstrate that, by using the learned statistics, we can estimate the spherical harmonic basis images from just one image taken under arbitrary illumination conditions if there is no pose variation. Compared to the first method, the second method builds the statistical models directly in 3D spaces by combining the spherical harmonic illumination representation and a 3D morphable model of human faces to recover basis images from images across both poses and illuminations. After estimating the basis images, we use the same recognition scheme for both methods: we recognize the face for which there exists a weighted combination of basis images that is the closest to the test face image. We provide a series of experiments that achieve high recognition rates, under a wide range of illumination conditions, including multiple sources of illumination. Our methods achieve comparable levels of accuracy with methods that have much more onerous training data requirements. Comparison of the two methods is also provided. View full abstract»

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

    Page(s): 364 - 376
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 unsupervised, information-theoretic, adaptive filter (UINTA) that improves the predictability of pixel intensities from their neighborhoods by decreasing their joint entropy. In this way, UINTA automatically discovers the statistical properties of the signal and can thereby restore a wide spectrum of images. The paper describes the formulation to minimize the joint entropy measure and presents several important practical considerations in estimating neighborhood statistics. It presents a series of results on both real and synthetic data along with comparisons with state-of-the-art techniques, including novel applications to medical image processing. View full abstract»

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  • Incremental nonlinear dimensionality reduction by manifold learning

    Page(s): 377 - 391
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 types of high-dimensional data by performing nonlinear dimensionality reduction. Most of these algorithms operate in a "batch" mode and cannot be efficiently applied when data are collected sequentially. In this paper, we describe an incremental version of ISOMAP, one of the key manifold learning algorithms. Our experiments on synthetic data as well as real world images demonstrate that our modified algorithm can maintain an accurate low-dimensional representation of the data in an efficient manner. View full abstract»

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

    Page(s): 392 - 402
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 supervised learning algorithms taking into account 1) the result of pairwise statistical tests on expected error (what the data tells us), and 2) our prior preferences, e.g., due to complexity. We define the problem in graph-theoretic terms and propose an algorithm to find the "best" learning algorithm in terms of these two criteria, or in the more general case, order learning algorithms in terms of their "goodness." Simulation results using five classification algorithms on 30 data sets indicate the utility of the method. Our proposed method can be generalized to regression and other loss functions by using a suitable pairwise test. View full abstract»

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  • Nonsmooth nonnegative matrix factorization (nsNMF)

    Page(s): 403 - 415
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    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 represent sparseness, in the form of nonsmoothness, which is controlled by a single parameter. In general, this method produces a set of basis and encoding vectors that are not only capable of representing the original data, but they also extract highly focalized patterns, which generally lend themselves to improved interpretability. The properties of this new method are illustrated with several data sets. Comparisons to previously published methods show that the new nsNMF method has some advantages in keeping faithfulness to the data in the achieving a high degree of sparseness for both the estimated basis and the encoding vectors and in better interpretability of the factors. View full abstract»

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  • Generic object recognition with boosting

    Page(s): 416 - 431
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 to combine them into one final hypothesis for each visual category. This combination of individual extractors and descriptors leads to recognition rates that are superior to other approaches which use only one specific extractor/descriptor setting. To explore the limitation of our system, we had to set up new, highly complex image databases that show the objects of interest at varying scales and poses, in cluttered background, and under considerable occlusion. We obtain classification results up to 81 percent ROC-equal error rate on the most complex of our databases. Our approach outperforms all comparable solutions on common databases. View full abstract»

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  • Real-time range acquisition by adaptive structured light

    Page(s): 432 - 445
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 suitable patterns at startup. A continuous trade-off between speed and quality is made. A weighted combination of different coding cues - based upon pattern color, geometry, and tracking - yields a robust way to solve the correspondence problem. The individual coding cues are automatically adapted within a considered family of patterns. The weights to combine them are based on the average consistency with the result within a small time-window. The integration itself is done by reformulating the problem as a graph cut. Also, the camera-projector configuration is taken into account for generating the projection patterns. The correctness of the range maps is not guaranteed, but an estimation of the uncertainty is provided for each part of the reconstruction. Our prototype is implemented using unmodified consumer hardware only and, therefore, is cheap. Frame rates vary between 10 and 25 fps, dependent on scene complexity. View full abstract»

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  • Relief texture from specularities

    Page(s): 446 - 457
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    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 prediction. We present a novel approach for measuring the relief texture of specular or partially specular surfaces using a specialized imaging device with a concave parabolic mirror to view multiple angles in a single image. Laser scanning typically fails for specular surfaces because of light scattering, but our method is explicitly designed for specular surfaces. Also, the spatial resolution of the measured geometry is significantly higher than standard methods, so very small surface details are captured. Furthermore, spatially varying reflectance is measured simultaneously, i.e., both texture color and texture shape are retrieved. View full abstract»

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

    Page(s): 458 - 462
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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

    Page(s): 463 - 468
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 segment the new image. The Gaussian mixture distribution that is used to model the current frame is transformed into a conjugate-prior distribution for the parametric model describing the segmentation of the new frame. This semisupervised method improves the segmentation quality and consistency and enables a propagation of segments along the segmented images. The performance of the proposed approach is illustrated on both simulated and real image data. View full abstract»

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  • Isoperimetric graph partitioning for image segmentation

    Page(s): 469 - 475
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    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

    Page(s): 476 - 480
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 machine (OCNM) and we show its properties. In particular, the OCNM solution asymptotically converges to the exact minimum volume set prespecified. Finally, numerical results illustrating the advantage of the new method are shown. View full abstract»

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  • Minimum reliable scale selection in 3D

    Page(s): 481 - 487
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    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 method is applied to 3D boundary detection and is illustrated in examples from biomedical imaging. The experimental results show that the 3D scale selection improves the detection of edges over single scale operators using as few as three different scales. View full abstract»

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  • Mumford and Shah functional: VLSI analysis and implementation

    Page(s): 487 - 494
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    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 results obtained for its complete implementation on a 0.13 μm standard cells technology are presented. View full abstract»

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    Page(s): 495
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  • TPAMI Information for authors

    Page(s): c3
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  • [Back cover]

    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.

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Editor-in-Chief
David A. Forsyth
University of Illinois