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IEEE Transactions on Pattern Analysis and Machine Intelligence

Issue 8 • Aug. 2005

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Displaying Results 1 - 19 of 19
  • [Front cover]

    Publication Year: 2005, Page(s): c1
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  • [Inside front cover]

    Publication Year: 2005, Page(s): c2
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  • Online clustering algorithms for radar emitter classification

    Publication Year: 2005, Page(s):1185 - 1196
    Cited by:  Papers (40)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1208 KB) | HTML iconHTML

    Radar emitter classification is a special application of data clustering for classifying unknown radar emitters from received radar pulse samples. The main challenges of this task are the high dimensionality of radar pulse samples, small sample group size, and closely located radar pulse clusters. In this paper, two new online clustering algorithms are developed for radar emitter classification: O... View full abstract»

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  • General C-means clustering model

    Publication Year: 2005, Page(s):1197 - 1211
    Cited by:  Papers (68)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1260 KB) | HTML iconHTML

    Partitional clustering is an important part of cluster analysis. Based on various theories, numerous clustering algorithms have been developed, and new clustering algorithms continue to appear in the literature. It is known that Occam's razor plays a pivotal role in data-based models, and partitional clustering is categorized as a data-based model. However, no relation had previously been discover... View full abstract»

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  • A scale space approach for automatically segmenting words from historical handwritten documents

    Publication Year: 2005, Page(s):1212 - 1225
    Cited by:  Papers (70)  |  Patents (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1664 KB) | HTML iconHTML

    Many libraries, museums, and other organizations contain large collections of handwritten historical documents, for example, the papers of early presidents like George Washington at the Library of Congress. The first step in providing recognition/retrieval tools is to automatically segment handwritten pages into words. State of the art segmentation techniques like the gap metrics algorithm have be... View full abstract»

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  • Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy

    Publication Year: 2005, Page(s):1226 - 1238
    Cited by:  Papers (2234)  |  Patents (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1265 KB) | HTML iconHTML

    Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first derive an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order... View full abstract»

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  • Generalizing Swendsen-Wang to sampling arbitrary posterior probabilities

    Publication Year: 2005, Page(s):1239 - 1253
    Cited by:  Papers (76)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2186 KB) | HTML iconHTML

    Many vision tasks can be formulated as graph partition problems that minimize energy functions. For such problems, the Gibbs sampler provides a general solution but is very slow, while other methods, such as Ncut and graph cuts are computationally effective but only work for specific energy forms and are not generally applicable. In this paper, we present a new inference algorithm that generalizes... View full abstract»

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  • Example-based photometric stereo: shape reconstruction with general, varying BRDFs

    Publication Year: 2005, Page(s):1254 - 1264
    Cited by:  Papers (120)  |  Patents (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1958 KB) | HTML iconHTML

    This paper presents a technique for computing the geometry of objects with general reflectance properties from images. For surfaces with varying material properties, a full segmentation into different material types is also computed. It is assumed that the camera viewpoint is fixed, but the illumination varies over the input sequence. It is also assumed that one or more example objects with simila... View full abstract»

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  • A sparse texture representation using local affine regions

    Publication Year: 2005, Page(s):1265 - 1278
    Cited by:  Papers (485)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1809 KB) | HTML iconHTML

    This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and nonrigid deformations. At the feature extraction stage, a sparse set of affine Harris and Laplacian regions is found in the image. Each of these regions can be thought of as a texture element having a characteristic elliptic shap... View full abstract»

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  • Time-to-collision estimation from motion based on primate visual processing

    Publication Year: 2005, Page(s):1279 - 1291
    Cited by:  Papers (11)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1437 KB) | HTML iconHTML

    A population coded algorithm, built on established models of motion processing in the primate visual system, computes the time-to-collision of a mobile robot to real-world environmental objects from video imagery. A set of four transformations starts with motion energy, a spatiotemporal frequency based computation of motion features. The following processing stages extract image velocity features ... View full abstract»

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  • Sparse Bayesian learning for efficient visual tracking

    Publication Year: 2005, Page(s):1292 - 1304
    Cited by:  Papers (114)  |  Patents (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1143 KB) | HTML iconHTML

    This paper extends the use of statistical learning algorithms for object localization. It has been shown that object recognizers using kernel-SVMs can be elegantly adapted to localization by means of spatial perturbation of the SVM. While this SVM applies to each frame of a video independently of other frames, the benefits of temporal fusion of data are well-known. This is addressed here by using ... View full abstract»

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  • Alignment of continuous video onto 3D point clouds

    Publication Year: 2005, Page(s):1305 - 1318
    Cited by:  Papers (51)  |  Patents (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3146 KB) | HTML iconHTML

    We propose a general framework for aligning continuous (oblique) video onto 3D sensor data. We align a point cloud computed from the video onto the point cloud directly obtained from a 3D sensor. This is in contrast to existing techniques where the 2D images are aligned to a 3D model derived from the 3D sensor data. Using point clouds enables the alignment for scenes full of objects that are diffi... View full abstract»

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  • Dental biometrics: alignment and matching of dental radiographs

    Publication Year: 2005, Page(s):1319 - 1326
    Cited by:  Papers (38)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2275 KB) | HTML iconHTML

    Dental biometrics utilizes dental radiographs for human identification. The dental radiographs provide information about teeth, including tooth contours, relative positions of neighboring teeth, and shapes of the dental work (e.g., crowns, fillings, and bridges). The proposed system has two main stages: feature extraction and matching. The feature extraction stage uses anisotropic diffusion to enh... View full abstract»

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  • Geometric properties of central catadioptric line images and their application in calibration

    Publication Year: 2005, Page(s):1327 - 1333
    Cited by:  Papers (75)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1116 KB) | HTML iconHTML

    In central catadioptric systems lines in a scene are projected to conic curves in the image. This work studies the geometry of the central catadioptric projection of lines and its use in calibration. It is shown that the conic curves where the lines are mapped possess several projective invariant properties. From these properties, it follows that any central catadioptric system can be fully calibr... View full abstract»

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  • On the Euclidean distance of images

    Publication Year: 2005, Page(s):1334 - 1339
    Cited by:  Papers (123)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (679 KB) | HTML iconHTML

    We present a new Euclidean distance for images, which we call image Euclidean distance (IMED). Unlike the traditional Euclidean distance, IMED takes into account the spatial relationships of pixels. Therefore, it is robust to small perturbation of images. We argue that IMED is the only intuitively reasonable Euclidean distance for images. IMED is then applied to image recognition. The key advantag... View full abstract»

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  • On FastMap and the convex hull of multivariate data: toward fast and robust dimension reduction

    Publication Year: 2005, Page(s):1340 - 1343
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (244 KB) | HTML iconHTML

    FastMap is a dimension reduction technique that operates on distances between objects. Although only distances are used, implicitly the technique assumes that the objects are points in a p-dimensional Euclidean space. It selects a sequence of k ≤ p orthogonal axes defined by distant pairs of points (called pivots) and computes the projection of the points onto the orthogonal axes. We show that ... View full abstract»

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  • Genetic-based EM algorithm for learning Gaussian mixture models

    Publication Year: 2005, Page(s):1344 - 1348
    Cited by:  Papers (99)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (546 KB) | HTML iconHTML

    We propose a genetic-based expectation-maximization (GA-EM) algorithm for learning Gaussian mixture models from multivariate data. This algorithm is capable of selecting the number of components of the model using the minimum description length (MDL) criterion. Our approach benefits from the properties of genetic algorithms (GA) and the EM algorithm by combination of both into a single procedure. ... View full abstract»

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  • TPAMI Information for authors

    Publication Year: 2005, Page(s): c3
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  • [Back cover]

    Publication Year: 2005, Page(s): c4
    Request permission for commercial reuse | PDF file iconPDF (143 KB)
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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