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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 11 • Date Nov 1999

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Displaying Results 1 - 15 of 15
  • Statistical region snake-based segmentation adapted to different physical noise models

    Page(s): 1145 - 1157
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (832 KB)  

    Algorithms for object segmentation are crucial in many image processing applications. During past years, active contour models (snakes) have been widely used for finding the contours of objects. This segmentation strategy is classically edge-based in the sense that the snake is driven to fit the maximum of an edge map of the scene. We propose a region snake approach and we determine fast algorithms for the segmentation of an object in an image. The algorithms developed in a maximum likelihood approach are based on the calculation of the statistics of the inner and the outer regions (defined by the snake). It has thus been possible to develop optimal algorithms adapted to the random fields which describe the gray levels in the input image if we assume that their probability density function family are known. We demonstrate that this approach is still efficient when no boundary's edge exists in the image. We also show that one can obtain fast algorithms by transforming the summations over a region, for the calculation of the statistics, into summations along the boundary of the region. Finally, we will provide numerical simulation results for different physical situations in order to illustrate the efficiency of this approach View full abstract»

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  • Meta analysis of classification algorithms for pattern recognition

    Page(s): 1137 - 1144
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (496 KB)  

    Various classification algorithms became available due to a surge of interdisciplinary research interests in the areas of data mining and knowledge discovery. We develop a statistical meta-model which compares the classification performances of several algorithms in terms of data characteristics. This empirical model is expected to aid decision making processes of finding the best classification tool in the sense of providing the minimum classification error among alternatives View full abstract»

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  • On the representation of image structures via scale space entropy conditions

    Page(s): 1199 - 1203
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (416 KB)  

    This paper deals with a novel way for representing and computing image features encapsulated within different regions of scale-space. Employing a thermodynamical model for scale-space generation, the method derives features as those corresponding to “entropy rich” image regions where, within a given range of spatial scales, the entropy gradient remains constant. Different types of image features, defining regions of different information content, are accordingly encoded by such regions within different bands of spatial scale View full abstract»

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  • Stochastic jump-diffusion process for computing medial axes in Markov random fields

    Page(s): 1158 - 1169
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (620 KB)  

    Proposes a statistical framework for computing medial axes of 2D shapes. In the paper, the computation of medial axes is posed as a statistical inference problem not as a mathematical transform. The paper contributes to three aspects in computing medial axes. 1) Prior knowledge is adopted for axes and junctions so that axes around junctions are regularized. 2) Multiple interpretations of axes are possible, each being assigned a probability. 3) A stochastic jump-diffusion process is proposed for estimating both axes and junctions in Markov random fields. We argue that the stochastic algorithm for computing medial axes is compatible with existing algorithms for image segmentation, such as region growing, snake, and region competition. Thus, our method provides a new direction for computing medial axes from texture images. Experiments are demonstrated on both synthetic and real 2D shapes View full abstract»

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  • Matching hierarchical structures using association graphs

    Page(s): 1105 - 1120
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (940 KB)  

    It is well-known that the problem of matching two relational structures can be posed as an equivalent problem of finding a maximal clique in a (derived) “association graph.” However, it is not clear how to apply this approach to computer vision problems where the graphs are hierarchically organized, i.e., are trees, since maximal cliques are not constrained to preserve the partial order. We provide a solution to the problem of matching two trees by constructing the association graph using the graph-theoretic concept of connectivity. We prove that, in the new formulation, there is a one-to-one correspondence between maximal cliques and maximal subtree isomorphisms. This allows us to cast the tree matching problem as an indefinite quadratic program using the Motzkin-Straus theorem, and we use “replicator” dynamical systems developed in theoretical biology to solve it. Such continuous solutions to discrete problems are attractive because they can motivate analog and biological implementations. The framework is also extended to the matching of attributed trees by using weighted association graphs. We illustrate the power of the approach by matching articulated and deformed shapes described by shock trees View full abstract»

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  • Corner detection and interpretation on planar curves using fuzzy reasoning

    Page(s): 1204 - 1210
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (392 KB)  

    The problem of corner detection on planar curves is examined based on human perception of local graphic features. First, a set of fuzzy patterns of contour points are established. Then, corner detection is characterized as a fuzzy classification problem that contains three stages: evaluation, classification, and location. Compared with existing methods, the proposed approach is superior in that it explains the curve, instead of simple labeling, and it performs based on human perception. Experimental results on shapes of various complexities are presented. The performance with respect to noise is also addressed View full abstract»

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  • Textfinder: an automatic system to detect and recognize text in images

    Page(s): 1224 - 1229
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (432 KB)  

    A robust system is proposed to automatically detect and extract text in images from different sources, including video, newspapers, advertisements, stock certificates, photographs, and checks. Text is first detected using multiscale texture segmentation and spatial cohesion constraints, then cleaned up and extracted using a histogram-based binarization algorithm. An automatic performance evaluation scheme is also proposed View full abstract»

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  • Using diagram generation software to improve diagram recognition: a case study of music notation

    Page(s): 1121 - 1136
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (888 KB)  

    Diagrams are widely used in society to transmit information such as circuit designs, music, mathematical formulae, architectural plans, and molecular structure. Computers must process diagrams both as images (marks on paper) and as information. A diagram recognizer translates from image to information and a diagram generator translates from information to image. Current technology for diagram generation is ahead of the technology for diagram recognition. Diagram generators have extensive knowledge of notational conventions which relate to readability and aesthetics, whereas current diagram recognizers focus on the hard constraints of the notation. To create a recognizer capable of exploiting layout information, it is expedient to reuse the expertise in existing diagram generators. In particular, we discuss the use of Lime (our editor and generator for music notation) to proofread and correct the raw output of MIDIScan (a third-party commercial recognizer for music notation). Over the past several years, this combination of software has been distributed to thousands of users View full abstract»

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  • Embedding Gestalt laws in Markov random fields

    Page(s): 1170 - 1187
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (924 KB)  

    The goal of this paper is to study a mathematical framework of 2D object shape modeling and learning for middle level vision problems, such as image segmentation and perceptual organization. For this purpose, we pursue generic shape models which characterize the most common features of 2D object shapes. In this paper, shape models are learned from observed natural shapes based on a minimax entropy learning theory. The learned shape models are Gibbs distributions defined on Markov random fields (MRFs). The neighborhood structures of these MRFs correspond to Gestalt laws-colinearity, cocircularity, proximity, parallelism, and symmetry. Thus, both contour-based and region-based features are accounted for. Stochastic Markov chain Monte Carlo (MCMC) algorithms are proposed for learning and model verification. Furthermore, this paper provides a quantitative measure for the so-called nonaccidental statistics and, thus, justifies some empirical observations of Gestalt psychology by information theory. Our experiments also demonstrate that global shape properties can arise from interactions of local features View full abstract»

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  • Affine reconstruction of curved surfaces from uncalibrated views of apparent contours

    Page(s): 1188 - 1198
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (880 KB)  

    In this paper, we consider uncalibrated reconstruction of curved surfaces from apparent contours. Since apparent contours are not fixed features (viewpoint independent), we cannot directly apply the recent results of the uncalibrated reconstruction from fixed features. We show that, nonetheless, curved surfaces can be reconstructed up to an affine ambiguity from their apparent contours viewed from uncalibrated cameras with unknown linear translations. Furthermore, we show that, even if the reconstruction is nonmetric (non-Euclidean), we can still extract useful information for many computer vision applications just from the apparent contours. We first show that if the camera motion is linear translation (but arbitrary direction and magnitude), the epipolar geometry can be recovered from the apparent contours without using any optimization process. The extracted epipolar geometry is next used for reconstructing curved surfaces from the deformations of the apparent contours viewed from uncalibrated cameras. The result is applied to distinguishing curved surfaces from fixed features in images. It is also shown that the time-to-contact to the curved surfaces can be computed from simple measurements of the apparent contours View full abstract»

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  • Color and illuminant voting

    Page(s): 1210 - 1215
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (412 KB)  

    A geometric-vision approach to color constancy and illuminant estimation is presented in this paper. We show a general framework, based on ideas from the generalized probabilistic Hough transform, to estimate the illuminant and reflectance of natural images. Each image pixel “votes” for possible illuminants and the estimation is based on cumulative votes. The framework is natural for the introduction of physical constraints in the color constancy problem. We show the relationship of this work to previous algorithms for color constancy and present examples View full abstract»

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  • Reliable determination of object pose from line features by hypothesis testing

    Page(s): 1235 - 1241
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (492 KB)  

    To develop a reliable computer vision system, the employed algorithm must guarantee good output quality. In this study, to ensure the quality of the pose estimated from line features, two simple test functions based on statistical hypothesis testing are defined. First, an error function based on the relation between the line features and some quality thresholds is defined. By using the first test function defined by a lower bound of the error function, poor input can be detected before estimating the pose. After pose estimation, the second test function can be used to decide if the estimated result is sufficiently accurate. Experimental results show that the first test function can detect input with low qualities or erroneous line correspondences and that the overall proposed method yields reliable estimated results View full abstract»

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  • RANSAC-based DARCES: a new approach to fast automatic registration of partially overlapping range images

    Page(s): 1229 - 1234
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (436 KB)  

    In this paper, we propose a new method, the RANSAC-based DARCES method (data-aligned rigidity-constrained exhaustive search based on random sample consensus), which can solve the partially overlapping 3D registration problem without any initial estimation. For the noiseless case, the basic algorithm of our method can guarantee that the solution it finds is the true one, and its time complexity can be shown to be relatively low. An extra characteristic is that our method can be used even for the case that there are no local features in the 3D data sets View full abstract»

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  • Robust line fitting in a noisy image by the method of moments

    Page(s): 1216 - 1223
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (348 KB)  

    The standard least squared distance method of fitting a line to a set of data points is known to be unreliable when the random noise in the input is significant compared with the data correlated to the line itself. Here, we present a new statistical clustering method based on Legendre moment theory and maximum entropy principle for line fitting in a noisy image. We propose a new approach for estimating the underlying probability density function (p.d.f.) of the data set. The p.d.f. is expanded in terms of Legendre polynomials by means of the Legendre moments. The order of the expansion is selected according to the maximum entropy principle. Then, the points corresponding to the maxima of the p.d.f. will be the true points of the line to be extracted by a chaining algorithm. This approach is directly generalized to multidimensional data. The proposed algorithm was successfully applied to real and simulated noisy line images, with comparison to some well-known methods View full abstract»

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  • Tracking human motion in structured environments using a distributed-camera system

    Page(s): 1241 - 1247
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (464 KB)  

    This paper presents a comprehensive framework for tracking coarse human models from sequences of synchronized monocular grayscale images in multiple camera coordinates. It demonstrates the feasibility of an end-to-end person tracking system using a unique combination of motion analysis on 3D geometry in different camera coordinates and other existing techniques in motion detection, segmentation, and pattern recognition. The system starts with tracking from a single camera view. When the system predicts that the active camera will no longer have a good view of the subject of interest, tracking will be switched to another camera which provides a better view and requires the least switching to continue tracking. The nonrigidity of the human body is addressed by matching points of the middle line of the human image, spatially and temporally, using Bayesian classification schemes. Multivariate normal distributions are employed to model class-conditional densities of the features for tracking, such as location, intensity, and geometric features. Limited degrees of occlusion are tolerated within the system. Experimental results using a prototype system are presented and the performance of the algorithm is evaluated to demonstrate its feasibility for real time applications View full abstract»

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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