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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 6 • Date June 2007

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Displaying Results 1 - 22 of 22
  • [Front cover]

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

    Page(s): c2
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  • Introduction of New Associate Editors

    Page(s): 913 - 914
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  • Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

    Page(s): 915 - 928
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3279 KB) |  | HTML iconHTML  

    Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences of the local binary patterns on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events such as facial expressions in which local information and its spatial locations should also be taken into account. In experiments with two DT databases, DynTex and Massachusetts Institute of Technology (MIT), both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the Cohn-Kanade facial expression database with excellent results. The advantages of our approach include local processing, robustness to monotonic gray-scale changes, and simple computation View full abstract»

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  • Toward Objective Evaluation of Image Segmentation Algorithms

    Page(s): 929 - 944
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2705 KB) |  | HTML iconHTML  

    Unsupervised image segmentation is an important component in many image understanding algorithms and practical vision systems. However, evaluation of segmentation algorithms thus far has been largely subjective, leaving a system designer to judge the effectiveness of a technique based only on intuition and results in the form of a few example segmented images. This is largely due to image segmentation being an ill-defined problem-there is no unique ground-truth segmentation of an image against which the output of an algorithm may be compared. This paper demonstrates how a recently proposed measure of similarity, the normalized probabilistic rand (NPR) index, can be used to perform a quantitative comparison between image segmentation algorithms using a hand-labeled set of ground-truth segmentations. We show that the measure allows principled comparisons between segmentations created by different algorithms, as well as segmentations on different images. We outline a procedure for algorithm evaluation through an example evaluation of some familiar algorithms - the mean-shift-based algorithm, an efficient graph-based segmentation algorithm, a hybrid algorithm that combines the strengths of both methods, and expectation maximization. Results are presented on the 300 images in the publicly available Berkeley segmentation data set View full abstract»

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  • Curve/Surface Representation and Evolution Using Vector Level Sets with Application to the Shape-Based Segmentation Problem

    Page(s): 945 - 958
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6344 KB) |  | HTML iconHTML  

    In this paper, we revisit the implicit front representation and evolution using the vector level set function (VLSF) proposed in (H. E. Abd El Munim, et al., Oct. 2005). Unlike conventional scalar level sets, this function is designed to have a vector form. The distance from any point to the nearest point on the front has components (projections) in the coordinate directions included in the vector function. This kind of representation is used to evolve closed planar curves and 3D surfaces as well. Maintaining the VLSF property as the distance projections through evolution will be considered together with a detailed derivation of the vector partial differential equation (PDE) for such evolution. A shape-based segmentation framework will be demonstrated as an application of the given implicit representation. The proposed level set function system will be used to represent shapes to give a dissimilarity measure in a variational object registration process. This kind of formulation permits us to better control the process of shape registration, which is an important part in the shape-based segmentation framework. The method depends on a set of training shapes used to build a parametric shape model. The color is taken into consideration besides the shape prior information. The shape model is fitted to the image volume by registration through an energy minimization problem. The approach overcomes the conventional methods problems like point correspondences and weighing coefficients tuning of the evolution (PDEs). It is also suitable for multidimensional data and computationally efficient. Results in 2D and 3D of real and synthetic data will demonstrate the efficiency of the framework View full abstract»

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  • Matching by Linear Programming and Successive Convexification

    Page(s): 959 - 975
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (9354 KB) |  | HTML iconHTML  

    We present a novel convex programming scheme to solve matching problems, focusing on the challenging problem of matching in a large search range and with cluttered background. Matching is formulated as metric labeling with L1 regularization terms, for which we propose a novel linear programming relaxation method and an efficient successive convexification implementation. The unique feature of the proposed relaxation scheme is that a much smaller set of basis labels is used to represent the original label space. This greatly reduces the size of the searching space. A successive convexification scheme solves the labeling problem in a coarse to fine manner. Importantly, the original cost function is reconvexified at each stage, in the new focus region only, and the focus region is updated so as to refine the searching result. This makes the method well-suited for large label set matching. Experiments demonstrate successful applications of the proposed matching scheme in object detection, motion estimation, and tracking View full abstract»

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  • Model-Based Tracking by Classification in a Tiny Discrete Pose Space

    Page(s): 976 - 989
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3068 KB) |  | HTML iconHTML  

    A method is presented for tracking 3D objects as they transform rigidly in space within a sparse range image sequence. The method operates in discrete space and exploits the coherence across image frames that results from the relationship between known bounds on the object's velocity and the sensor frame rate. These motion bounds allow the interframe transformation space to be reduced to a reasonable and indeed tiny size, comprising only tens or hundreds of possible states. The tracking problem is in this way cast into a classification framework, effectively trading off localization precision for runtime efficiency and robustness. The method has been implemented and tested extensively on a variety of freeform objects within a sparse range data stream comprising only a few hundred points per image. It has been shown to compare favorably against continuous domain iterative closest point (ICP) tracking methods, performing both more efficiently and more robustly. A hybrid method has also been implemented that executes a small number of ICP iterations following the initial discrete classification phase. This hybrid method is both more efficient than the ICP alone and more robust than either the discrete classification method or the ICP separately View full abstract»

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  • Mask-Based Second-Generation Connectivity and Attribute Filters

    Page(s): 990 - 1004
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1876 KB) |  | HTML iconHTML  

    Connected filters are edge-preserving morphological operators, which rely on a notion of connectivity. This is usually the standard 4 and 8-connectivity, which is often too rigid since it cannot model generalized groupings such as object clusters or partitions. In the set-theoretical framework of connectivity, these groupings are modeled by the more general second-generation connectivity. In this paper, we present both an extension of this theory, and provide an efficient algorithm based on the max-tree to compute attribute filters based on these connectivities. We first look into the drawbacks of the existing framework that separates clustering and partitioning and is directly dependent on the properties of a preselected operator. We then propose a new type of second-generation connectivity termed mask-based connectivity which eliminates all previous dependencies and extends the ways the image domain can be connected. A previously developed dual-input max-tree algorithm for area openings is adapted for the wider class of attribute filters on images characterized by second-generation connectivity. CPU-times for the new algorithm are comparable to the original algorithm, typically deviating less than 10 percent either way View full abstract»

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  • Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations

    Page(s): 1005 - 1018
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1913 KB) |  | HTML iconHTML  

    We address the problem of comparing sets of images for object recognition, where the sets may represent variations in an object's appearance due to changing camera pose and lighting conditions. canonical correlations (also known as principal or canonical angles), which can be thought of as the angles between two d-dimensional subspaces, have recently attracted attention for image set matching. Canonical correlations offer many benefits in accuracy, efficiency, and robustness compared to the two main classical methods: parametric distribution-based and nonparametric sample-based matching of sets. Here, this is first demonstrated experimentally for reasonably sized data sets using existing methods exploiting canonical correlations. Motivated by their proven effectiveness, a novel discriminative learning method over sets is proposed for set classification. Specifically, inspired by classical linear discriminant analysis (LDA), we develop a linear discriminant function that maximizes the canonical correlations of within-class sets and minimizes the canonical correlations of between-class sets. Image sets transformed by the discriminant function are then compared by the canonical correlations. Classical orthogonal subspace method (OSM) is also investigated for the similar purpose and compared with the proposed method. The proposed method is evaluated on various object recognition problems using face image sets with arbitrary motion captured under different illuminations and image sets of 500 general objects taken at different views. The method is also applied to object category recognition using ETH-80 database. The proposed method is shown to outperform the state-of-the-art methods in terms of accuracy and efficiency View full abstract»

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  • Recognition of Pornographic Web Pages by Classifying Texts and Images

    Page(s): 1019 - 1034
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1559 KB) |  | HTML iconHTML  

    With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the object's contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages View full abstract»

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  • Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications

    Page(s): 1035 - 1051
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3499 KB) |  | HTML iconHTML  

    RELIEF is considered one of the most successful algorithms for assessing the quality of features. In this paper, we propose a set of new feature weighting algorithms that perform significantly better than RELIEF, without introducing a large increase in computational complexity. Our work starts from a mathematical interpretation of the seemingly heuristic RELIEF algorithm as an online method solving a convex optimization problem with a margin-based objective function. This interpretation explains the success of RELIEF in real application and enables us to identify and address its following weaknesses. RELIEF makes an implicit assumption that the nearest neighbors found in the original feature space are the ones in the weighted space and RELIEF lacks a mechanism to deal with outlier data. We propose an iterative RELIEF (I-RELIEF) algorithm to alleviate the deficiencies of RELIEF by exploring the framework of the expectation-maximization algorithm. We extend I-RELIEF to multiclass settings by using a new multiclass margin definition. To reduce computational costs, an online learning algorithm is also developed. Convergence analysis of the proposed algorithms is presented. The results of large-scale experiments on the UCI and microarray data sets are reported, which demonstrate the effectiveness of the proposed algorithms, and verify the presented theoretical results View full abstract»

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  • MonoSLAM: Real-Time Single Camera SLAM

    Page(s): 1052 - 1067
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1966 KB) |  | HTML iconHTML  

    We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to structure from motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live augmented reality with a hand-held camera View full abstract»

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  • Stereo Correspondence with Occlusion Handling in a Symmetric Patch-Based Graph-Cuts Model

    Page(s): 1068 - 1079
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1589 KB) |  | HTML iconHTML  

    A novel patch-based correspondence model is presented in this paper. Many segment-based correspondence approaches have been proposed in recent years. Untextured pixels and boundaries of discontinuities are imposed with hard constraints by the discontinuity assumption that large disparity variation only happens at the boundaries of segments in the above approaches. Significant improvements on performance of untextured and discontinuity area have been reported. But, the performance near occlusion is not satisfactory because a segmented region in one image may be only partially visible in the other one. To solve this problem, we utilize the observation that the shared edge of a visible area and an occluded area corresponds to the discontinuity in the other image. So, the proposed model conducts color segmentation on both images first and then a segment in one image is further cut into smaller patches corresponding to the boundaries of segments in the other when it is assigned with a disparity. Different visibility of patches in one segment is allowed. The uniqueness constraint in a segment level is used to compute the occlusions. An energy minimization framework using graph-cuts is proposed to find a global optimal configuration including both disparities and occlusions. Besides, some measurements are taken to make our segment-based algorithm suffer less from violation of the discontinuity assumption. Experimental results have shown superior performance of the proposed approach, especially on occlusions, untextured areas, and near discontinuities View full abstract»

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  • Focus Area Extraction by Blind Deconvolution for Defining Regions of Interest

    Page(s): 1080 - 1085
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    We present an automatic focus area estimation method, working with a single image without a priori information about the image, the camera, or the scene. It produces relative focus maps by localized blind deconvolution and a new residual error-based classification. Evaluation and comparison is performed and applicability is shown through image indexing View full abstract»

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  • The Bayes Decision Rule Induced Similarity Measures

    Page(s): 1086 - 1090
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    This paper first shows that the popular whitened cosine similarity measure is related to the Bayes decision rule under specific assumptions and then presents two new similarity measures: the PRM whitened cosine (PWC) similarity measure and the within-class whitened cosine (WWC) similarity measure. Experiments on face recognition using the Face Recognition Grand Challenge (FRGC) version 2 database show the effectiveness of the new measures View full abstract»

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  • A Normalized Levenshtein Distance Metric

    Page(s): 1091 - 1095
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    Although a number of normalized edit distances presented so far may offer good performance in some applications, none of them can be regarded as a genuine metric between strings because they do not satisfy the triangle inequality. Given two strings X and Y over a finite alphabet, this paper defines a new normalized edit distance between X and Y as a simple function of their lengths (|X| and |Y|) and the Generalized Levenshtein Distance (GLD) between them. The new distance can be easily computed through GLD with a complexity of O(|X| cdot |Y|) and it is a metric valued in [0, 1] under the condition that the weight function is a metric over the set of elementary edit operations with all costs of insertions/deletions having the same weight. Experiments using the AESA algorithm in handwritten digit recognition show that the new distance can generally provide similar results to some other normalized edit distances and may perform slightly better if the triangle inequality is violated in a particular data set. View full abstract»

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  • Space-Time Adaptation for Patch-Based Image Sequence Restoration

    Page(s): 1096 - 1102
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1443 KB) |  | HTML iconHTML  

    We present a novel space-time patch-based method for image sequence restoration. We propose an adaptive statistical estimation framework based on the local analysis of the bias-variance trade-off. At each pixel, the space-time neighborhood is adapted to improve the performance of the proposed patch-based estimator. The proposed method is unsupervised and requires no motion estimation. Nevertheless, it can also be combined with motion estimation to cope with very large displacements due to camera motion. Experiments show that this method is able to drastically improve the quality of highly corrupted image sequences. Quantitative evaluations on standard artificially noise-corrupted image sequences demonstrate that our method outperforms other recent competitive methods. We also report convincing results on real noisy image sequences View full abstract»

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  • In this issue - Technically

    Page(s): 1103
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  • 180,000 aritlces in the IEEE Computer Society Digital Library [advertisement]

    Page(s): 1104
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  • TPAMI Information for authors

    Page(s): c3
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  • [Back cover]

    Page(s): c4
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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