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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 1 • Date Jan 1994

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Displaying Results 1 - 13 of 13
  • Constrained regularized differentiation

    Page(s): 88 - 92
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (404 KB)  

    Numerical differentiation is an ill-posed problem. This article demonstrates that the application of the regularization theory together with the methods of projections on convex sets of constraints improve the accuracy of the derivatives calculation. Simulation results are presented and the applications of the proposed method to edge detection are discussed View full abstract»

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  • Reply to “On the localization performance measure and optimal edge detection”

    Page(s): 108 - 110
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (240 KB)  

    A discussion of the step edge model and the zero crossings of edge detectors is provided to justify the authors' previous analysis (see ibid., vol. 12, p.1186-1189) of the localization performance error. It is shown that the previous analysis is consistent with its assumptions. The exact solution to the zero crossing density of the edge detection filter is also provided View full abstract»

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  • A novel feature recognition neural network and its application to character recognition

    Page(s): 98 - 106
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    Presents a feature recognition network for pattern recognition that learns the patterns by remembering their different segments. The base algorithm for this network is a Boolean net algorithm that the authors developed during past research. Simulation results show that the network can recognize patterns after significant noise, deformation, translation and even scaling. The network is compared to existing popular networks used for the same purpose, especially the Neocognitron. The network is also analyzed as regards to interconnection complexity and information storage/retrieval View full abstract»

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  • “On the localization performance measure and optimal edge detection”

    Page(s): 106 - 108
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    Tagare and deFigueiredo (see ibid., vol. 12, p. 1186-1189, 1990 ) present a localization performance measure for edge detectors. They correctly point out a flaw in Canny's formulation of the localization criterion, which was subsequently adopted by Sarkar and Boyer (1991). They motivate their form of the localization criterion along a different line of reasoning. In this comment, the authors show that although Canny's derivation was in error, the final form of his criterion is adequate and can, in fact, be derived from Tagare and deFigueiredo's formulation of the problem. The authors also point out some problems with Tagare and deFigueiredo's localization criterion View full abstract»

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  • Decision combination in multiple classifier systems

    Page(s): 66 - 75
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (988 KB)  

    A multiple classifier system is a powerful solution to difficult pattern recognition problems involving large class sets and noisy input because it allows simultaneous use of arbitrary feature descriptors and classification procedures. Decisions by the classifiers can be represented as rankings of classifiers and different instances of a problem. The rankings can be combined by methods that either reduce or rerank a given set of classes. An intersection method and union method are proposed for class set reduction. Three methods based on the highest rank, the Borda count, and logistic regression are proposed for class set reranking. These methods have been tested in applications of degraded machine-printed characters and works from large lexicons, resulting in substantial improvement in overall correctness View full abstract»

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  • Supervised learning of descriptions for image recognition purposes

    Page(s): 92 - 98
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    This study deals with a learning system for generation of descriptions of objects to be recognized in 2-D images. After proposing a framework for handling fuzzy and relational descriptions, we present the system obtained by making such a framework manage a well-known learning methodology. Satisfactory results and comparisons are reported View full abstract»

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  • Part I: Modeling image curves using invariant 3-D object curve models-a path to 3-D recognition and shape estimation from image contours

    Page(s): 1 - 12
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    This paper and its companion are concerned with the problems of 3-D object recognition and shape estimation from image curves using a 3-D object curve model that is invariant to affine transformation onto the image space, and a binocular stereo imaging system. The objects of interest here are the ones that have markings (e.g., characters, letters, special drawings and symbols, etc.) on their surfaces. The 3-D curves on the object are modeled as B-splines, which are characterized by a set of parameters (the control points) from which the 3-D curve can be totally generated. The B-splines are invariant under affine transformations. That means that the affine projected object curve onto the image space is a B-spline whose control points are related to the object control points through the affine transformation. Part I deals with issues relating to the curve modeling process. In particular, the authors address the problems of estimating the control points from the data curve, and of deciding on the “best” order B-spline and the “best” number of control points to be used to model the image or object curve(s). A minimum mean-square error (mmse) estimation technique which is invariant to affine transformations is presented as a noniterative, simple, and fast approach for control point estimation. The “best” B-spline is decided upon using a Bayesian selection rule. Finally, we present a matching algorithm that allocates a sample curve to one of p prototype curves when the sample curve is an a priori unknown affine transformation of one of the prototype curves stored in the data base. The approach is tried on a variety of images of real objects View full abstract»

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  • Theory and practice of vector quantizers trained on small training sets

    Page(s): 54 - 65
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    Examines how the performance of a memoryless vector quantizer changes as a function of its training set size. Specifically, the authors study how well the training set distortion predicts test distortion when the training set is a randomly drawn subset of blocks from the test or training image(s). Using the Vapnik-Chervonenkis (VC) dimension, the authors derive formal bounds for the difference of test and training distortion of vector quantizer codebooks. The authors then describe extensive empirical simulations that test these bounds for a variety of codebook sizes and vector dimensions, and give practical suggestions for determining the training set size necessary to achieve good generalization from a codebook. The authors conclude that, by using training sets comprising only a small fraction of the available data, one can produce results that are close to the results obtainable when all available data are used View full abstract»

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  • A diffusion mechanism for obstacle detection from size-change information

    Page(s): 76 - 80
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    A mechanism fur the visual detection of obstacles is presented. A new immediacy measure, representing the imminence of collision between an object and a moving observer, is defined. A diffusion process on the image domain, whose initial condition is determined by the motion field normal to the object's boundary, is shown to converge asymptotically to the immediacy measure. A network of locally connected cells, derived from a finite-difference approximation of the diffusion equation, estimates the immediacy measure from normal velocity and boundary information provided by a motion measurement and segmentation stage. The algorithm's performance on real image sequences is demonstrated View full abstract»

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  • Gibbs random fields, cooccurrences, and texture modeling

    Page(s): 24 - 37
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    Gibbs random field (GRF) models and features from cooccurrence matrices are typically considered as separate but useful tools for texture discrimination. The authors show an explicit relationship between cooccurrences and a large class of GRF's. This result comes from a new framework based on a set-theoretic concept called the “aura set” and on measures of this set, “aura measures.” This framework is also shown to be useful for relating different texture analysis tools. The authors show how the aura set can be constructed with morphological dilation, how its measure yields cooccurrences, and how it can be applied to characterizing the behavior of the Gibbs model for texture. In particular, they show how the aura measure generalizes, to any number of gray levels and neighborhood order, some properties previously known for just the binary, nearest-neighbor GRF. Finally, the authors illustrate how these properties can guide one's intuition about the types of GRF patterns which are most likely to form View full abstract»

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  • N-folded symmetries by complex moments in Gabor space and their application to unsupervised texture segmentation

    Page(s): 80 - 87
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    Complex moments of the Gabor power spectrum yield estimates of the N-folded symmetry of the local image content at different frequency scales, that is, they allow to detect linear, rectangular, hexagonal/triangular, and so on, structures with very fine to very coarse resolutions. Results from experiments on the unsupervised segmentation of real textures indicate their importance for image processing applications. Real geometric moments computed in Gabor space also provide for very powerful texture features, but lack the clear geometrical interpretation of complex moments View full abstract»

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  • Describing complicated objects by implicit polynomials

    Page(s): 38 - 53
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    This paper introduces and focuses on two problems. First is the representation power of closed implicit polynomials of modest degree for curves in 2-D images and surfaces in 3-D range data. Super quadrics are a small subset of object boundaries that are well fitted by these polynomials. The second problem is the stable computationally efficient fitting of noisy data by closed implicit polynomial curves and surfaces. The attractive features of these polynomials for Vision is discussed View full abstract»

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  • Part II: 3-D object recognition and shape estimation from image contours using B-splines, shape invariant matching, and neural network

    Page(s): 13 - 23
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    For pt. I, see ibid., p.1-12 (1994). This paper is the second part of a 3-D object recognition and shape estimation system that identifies particular objects by recognizing the special markings (text, symbols, drawings, etc.) on their surfaces. The shape of the object is identified from the image curves using B-spline curve modeling as described in Part I, as well as a binocular stereo imaging system. This is achieved by first estimating the 3-D control points from the corresponding curves in each image in the stereo imaging system. From the 3-D control points, the 3-D object curves are generated, and these are subsequently used for estimating the 3-D surface parameters. A Bayesian framework is used for classifying the image into one of c possible surfaces based on the extracted 3-D object curves. This is complemented by a neural network (NN) that recognizes the surface as a particular object (e.g., a Pepsi can versus a peanut butter jar), by reading the text/markings on the surface. To reduce the amount of training the NN has to undergo for recognition, the object curves are “unwarped” into planar curves before the matching process. This eliminates the need for templates that are surface shape dependent and results in a planar curve that might be a rotated, translated, and scaled version of the template. Hence, for the matching process we need to use measures that are invariant to these transformations. One such measure is the Fourier descriptors (FD) derived from the control points associated with the unwarped parent curves. The approach is tried on a variety of images of real objects and appears to hold great promise View full abstract»

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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