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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 3 • Date May 1985

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

    Page(s): c1
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  • List of Contributors

    Page(s): nil1
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  • [Breaker page]

    Page(s): nil1
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  • Opinion

    Page(s): 245
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  • A Knowledge Based System for Analysis of Gated Blood Pool Studies

    Page(s): 246 - 259
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    A system for obtaining a complete diagnostic description of an image sequence taken in nuclear medicine from the human heart has been developed, implemented, and tested. The knowledge about these images is represented in a semantic net, conclusions are drawn by a production rule approach, and scoring of alternative diagnoses is based on fuzzy membership functions. On the low level, image pixels are smoothed and organ contours are extracted; these are the input for the high level processing. Tests with several image sequences gave correct descriptions as compared to the diagnosis of a physician. View full abstract»

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  • A Computational Approach to Approximate and Plausible Reasoning with Applications to Expert Systems

    Page(s): 260 - 283
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    The intended purpose of this paper is twofold: proposing a common basis for the modeling of uncertainty and imprecision, and discussing various kinds of approximate and plausible reasoning schemes in this framework. Together with probability, different kinds of uncertainty measures (credibility and plausibility functions in the sense of Shafer, possibility measures in the sense of Zadeh and the dual measures of necessity, Sugeno's g¿-fuzzy measures) are introduced in a unified way. The modeling of imprecision in terms of possibility distribution is then presented, and related questions such as the measure of the uncertainty of fuzzy events, the probability and possibility qualification of statements, the concept of a degree of truth, and the truth qualification of propositions, are discussed at length. Deductive inference from premises weighted by different kinds of measures by uncertainty, or by truth-values in the framework of various multivalued logics, is fully investigated. Then, deductive inferences from imprecise or fuzzy premises are dealt with; patterns of reasoning where both uncertainty and imprecision are present are also addressed. The last section is devoted to the combination of uncertain or imprecise pieces of information given by different sources. On the whole, this paper is a tentative survey of quantitative approaches in the modeling of uncertainty and imprecision including recent theoretical proposals as well as more empirical techniques such as the ones developed in expert systems such as MYCIN or PROSPECTOR, the management of uncertainty and imprecision in reasoning patterns being a key issue in artificial intelligence. View full abstract»

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  • Hierarchical Coding of Binary Images

    Page(s): 284 - 298
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    Quadtrees are a compact hierarchical method of representation of images. In this paper, we explore a number of hierarchical image representations as applied to binary images, of which quadtrees are a single exemplar. We discuss quadtrees, binary trees, and an adaptive hierarchical method. Extending these methods into the third dimension of time results in several other methods. All of these methods are discussed in terms of time complexity, worst case and average compression of random images, and compression results on binary images derived from natural scenes. The results indicate that quadtrees are the most effective for two-dimensional images, but the adaptive algorithms are more effective for dynamic image sequences. View full abstract»

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  • Waveform Correlation by Tree Matching

    Page(s): 299 - 305
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    A waveform correlation scheme is presented. The scheme consists of four parts: 1) the representation of waveforms by trees, 2) the definition of basic operations on tree nodes and tree distance, 3) a tree matching algorithm, and 4) a backtracking procedure to find the best node-to-node correlation. This correlation scheme has been implemented. Results show that the scheme has the capability of handling distortions that result from stretching or shrinking of intervals or from missing intervals. View full abstract»

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  • Space-Time Domain Expansion Approach to VLSI and Its Application to Hierarchical Scene Matching

    Page(s): 306 - 319
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    VLSI technology has recently received increasing attention due to its high performance and high reliability. Designing a VLSI structure systematically for a given task becomes a very important problem to many computer engineers. In this paper, we present a method to transform a recursive computation task into a VLSI structure systematically. The main advantages of this approach are its simplicity and completeness. Several examples, such as vector inner product, matrix multiplication, convolution, comparison operations in relational database and fast Fourier transformation (FFT), are given to demonstrate the transformation procedure. Finally, we apply the proposed method to hierarchical scene matching. Scene matching refers to the process of locating or matching a region of an image with a corresponding region of another view of the same image taken from a different viewing angle or at a different time. We first present a constant threshold estimation for hierarchical scene matching. The VLSI implementation of the hierarchical scene matching is then described in detail. View full abstract»

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  • The Recovery of Three-Dimensional Structure from Image Curves

    Page(s): 320 - 326
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    A class of inferences is described which allows the recovery of three-dimensional structures from the two-dimensional curves in an image. Unlike most previous methods, these inferences do not require restrictive assumptions or prior knowledge regarding the scene. They are based on the assumption that the camera viewpoint and the positions of the illumination sources are independent of the objects in the scene. From these independence assumptions, it can be shown that many potential interpretations of image curves are highly improbable. By eliminating these improbable interpretations it is possible to segment the image into sets of related image features and derive many three-space relations. View full abstract»

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  • The Wedge Filter Technique for Convex Boundary Estimation

    Page(s): 326 - 332
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    This paper describes a method for segmentation of convex shaped image regions. The wedge filter technique first employs the converging squares algorithm [1] to locate a region of interest. Then a region oriented boundary estimation technique, called the wedge filter, is applied. This wedge filter entails angular filtering and subsampling, and boundary interpolation. The technique is more capable of segmenting noncircular shapes than some earlier methods based on the Hough transform. In addition, unlike many edge-based segmentation schemes, this method is relatively tolerant to edge gaps and to blurred or thick edges. This technique is tested on a number of synthesized images over a range of convex shapes, for different algorithm parameters, and under various conditions of region size and image noise. In addition, the technique has been applied to segmentation of liver cell nuclei in light microscope images of human liver tissue. View full abstract»

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  • Contour Map Registration Using Fourier Descriptors of Gradient Codes

    Page(s): 332 - 338
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    To estimate the position of a subpicture P1 (with unknown angular misaligment) in a contour map P2, a method based on Fourier descriptors of multidirectional gradient codes is suggested. It is assumed that P2 is characterized by a set of magnitudes at equally spaceddiscrete points over a rectangular area; and P1 is described by a set of magnitudes at discrete points on directional axes emanated from a point with magnitude c*. Using the measurements of P1, the multidirectional gradient or successive-gradient codes and their Fourier descriptors are generated. A contour map for P2 having c* as one of the isopleth values is then obtained. For each point on all c*-isopleths, a two-level classifier, utilizing information derived from the Fourier descriptors and the phase correlation function, is used to estimate the possible location of P1 in P2. Simulation has indicated that in many cases the angular misalignment and the position of P1 with respect to P2 can be determined. View full abstract»

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  • Template Matching in Rotated Images

    Page(s): 338 - 344
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    A rotationally invariant template matching using normalized invariant moments is described. It is shown that if normalized invariant moments in circular windows are used, then template matching in rotated images becomes similar to template matching in translated images. A speedup technique based on the idea of two-stage template matching is also described. In this technique, the zeroth-order moment is used in the first stage to determine the likely match positions, and the second and third-order moments are used in the second stage to determine the best match position among the likely ones. View full abstract»

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  • Linear Quadtrees from Vector Representations of Polygons

    Page(s): 344 - 349
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    A new algorithm is presented which produces various forms of linear quadtrees directly from a vector representation of a polygon. This algorithm takes advantage of specific properties of linear quadtrees and associated linear keys to infer the colors of all parts of the region not cut by the polygon boundary. The method is further extended to multicolored (rather than binary) linear quadtrees which may be useful in geographic information systems applications. View full abstract»

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  • Automatic Feature Design for Optical Character Recognition Using an Evolutionary Search Procedure

    Page(s): 349 - 355
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    An automatic evolutionary search is applied to the problem of feature extraction in an OCR application. A performance measure based on feature independence is used to generate features which do not appear to suffer from peaking effects [17]. Features are extracted from a training set of 30 600 machine printed 34 class alphanumeric characters derived from British mail. Classification results on the training set and a test set of 10 200 characters are reported for an increasing number of features. A 1.01 percent forced decision error rate is obtained on the test data using 316 features. The hardware implementation should be cheap and fast to operate. The performance compares favorably with current low cost OCR page readers. View full abstract»

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  • Monotonicity of Linear Separability Under Translation

    Page(s): 355 - 358
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    A set of n pattern vectors are given in d-space and classified arbitrarily into two sets. The sets of patterns are said to be linearly separable if there exists a hyperplane that separates them. We ask whether translation of one of these sets in an arbitrary direction helps separability. Sometimes yes and sometimes no, but yes on the average. The average is taken over all classifications of the patterns into two sets. In fact, we prove that the probability of separability increases as the translation increases. Thus, we conclude that if points are drawn equiprobably from densities fo(x) and f1(x) = fo(x + tw) then the probability of linear separability is minimum at t = 0 and increases with t for t > 0. View full abstract»

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  • On the Effect of Noise on the Moore-Penrose Generalized Inverse Associative Memory

    Page(s): 358 - 360
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    Monte Carlo simulations of the continuous Moore-Penrose generalized inverse associative memory (Kohonen [l]) have shown that the noise-to-signal ratio is improved on recall in the autoassociative case as long as the number of vector pairs stored is less than the number of components per vector. In the heteroassociative case, however, the noise-to-signal ratio may actually be greatly increased upon recall, particularly as the number of vector pairs stored approaches the number of components per vector. The increase in output noise-to-signal ratio in the heteroassociative case is found to be due to the fact that the inverse of the product of the key vector matrix with its transpose may increase without bound in spite of the fact that the key vectors are linearly independent. View full abstract»

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  • Data Structures in Kernel Density Estimation

    Page(s): 360 - 366
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    We analyze and compare several data structures and algorithms for evaluating the kernel density estimate. Frequent evaluations of this estimate are for example needed for plotting, error estimation, Monte Carlo estimation of probabilities and functionals, and pattern classification. An experimental comparison is included. View full abstract»

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  • A Loose-Pattern Process Approach to Clustering Fuzzy Data Sets

    Page(s): 366 - 372
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    A loose-pattern process approach to clustering sets consists of three main computations: loose-pattern reject option, tight-pattern classifcation, and loose-pattern assigning classes. The loose-pattern rejection is implemented using a rule based on q nearest neighbors of each point. Two clustering methods, GLC and OUPIC, are introduced as tight-pattern clustering techniques. The decisions of loose-pattern assigning classes are related to a heuristic membership function. The function and experiments with one set is discussed. View full abstract»

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  • List of Contributors

    Page(s): nil2
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    Freely Available from IEEE
  • [Front cover]

    Page(s): c2
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    Freely Available from IEEE

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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Meet Our Editors

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