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IEEE Transactions on Pattern Analysis and Machine Intelligence

Issue 5 • Date Sept. 1981

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Displaying Results 1 - 18 of 18
  • [Front cover]

    Publication Year: 1981, Page(s): c1
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  • List of Contributors

    Publication Year: 1981, Page(s): nil1
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  • [Breaker page]

    Publication Year: 1981, Page(s): nil1
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  • Extraction of Motion Information from Peripheral Processes

    Publication Year: 1981, Page(s):489 - 503
    Cited by:  Papers (26)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4661 KB)

    This paper is mainly concerned with low-level processes in machine perception of motion. A motion analysis system should exploit information contained in ``early warning signals'' during the intensity based peripheral phase of motion perception. We show that intensity based difference pictures contain motion information about objects in a dynamic scene, and present methods for the extraction of mo... View full abstract»

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  • Structural Descriptions and Inexact Matching

    Publication Year: 1981, Page(s):504 - 519
    Cited by:  Papers (134)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3088 KB)

    In this paper we formally define the structural description of an object and the concepts of exact and inexact matching of two structural descriptions. We discuss the problems associated with a brute-force backtracking tree search for inexact matching and develop several different algorithms to make the tree search more efficient. We develop the formula for the expected number of nodes in the tree... View full abstract»

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  • A New Method for Texture Fields Synthesis: Some Applications to the Study of Human Vision

    Publication Year: 1981, Page(s):520 - 533
    Cited by:  Papers (27)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3572 KB)

    We present a general method to synthesize a stochastic process from its a priori given second-order statistics. Some new results, including a new conjecture dealing with visual discrimination of stochastic texture fields, are also given. View full abstract»

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  • Analysis of the Precision of Generalized Chain Codes for the Representation of Planar Curves

    Publication Year: 1981, Page(s):533 - 539
    Cited by:  Papers (32)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2299 KB)

    This paper examines a set of line-segment approximation codes for the representation of planar curves (the so-called generalized chain codes) and shows that the average quantization error (measure of code's precision) is directly proportional to the grid size and is independent of the form of the code. Thus, to achieve a desired level of precision for the representation of a line drawing, only the... View full abstract»

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  • A Modular Computer Vision System for Picture Segmentation and Interpretation

    Publication Year: 1981, Page(s):540 - 556
    Cited by:  Papers (17)  |  Patents (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (7737 KB)

    The objective of a computer vision system is to outline the objects in a picture and label them with an appropriate interpretation. This paper proposes a new paradigm for a modular computer vision system which is both data directed and knowledge based. The system consists of three different types of units, two of which are associative data memories implemented as relational databases. The short-te... View full abstract»

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  • Texture Discrimination Based Upon an Assumed Stochastic Texture Model

    Publication Year: 1981, Page(s):557 - 580
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (15553 KB)

    A new approach to texture discrimination is described. This approach is based upon an assumed stochastic model for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier. The construction and properties of the stochastic texture model are described and a digital filtering implementation of the resulting maximum likelihood texture discriminant is provi... View full abstract»

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  • Two New Edge Detectors

    Publication Year: 1981, Page(s):581 - 592
    Cited by:  Papers (8)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4861 KB)

    This paper introduces two new edge detection algorithms. One uses multiple difference-based edge detectors. This scheme selects peak center by absolute maximum or center of mass techniques. The other algorithm is motivated by the observation that second-order enhancement improves human contour extraction, but generally confuses difference-based edge detectors. This algorithm translates intensity i... View full abstract»

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  • Comments on ``Algorithms for Shape Analysis of Contours and Waveforms''

    Publication Year: 1981, Page(s): 593
    Cited by:  Papers (1)
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  • An Optimal Line Following Algorithm

    Publication Year: 1981, Page(s):593 - 598
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2320 KB)

    An algorithm is described for optimal following of the lines in digitized TV pictures. Initially, the picture is subjected to an edge detection operation resulting in short edge-line pieces (strokes). The bifurcation points of the lines in the picture are detected on the basis of variances in stroke angles. A mathematical model using graph theoretical concepts is set up on the arrays of strokes (s... View full abstract»

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  • Thresholding Using Relaxation

    Publication Year: 1981, Page(s):598 - 606
    Cited by:  Papers (15)  |  Patents (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (8245 KB)

    If a picture contains dark objects on a light background (or vice versa), the objects can be extracted by thresholding, i.e., by classifying the pixels into ``light'' and ``dark'' classes. If the picture is noisy, so that the object and background gray level populations overlap, there will be errors in the thresholded output. A relaxation process can be used to reduce these errors; we classify the... View full abstract»

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  • Computer Tracking of Moving Point Targets in Space

    Publication Year: 1981, Page(s):606 - 611
    Cited by:  Papers (55)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1990 KB)

    An adaptive algorithm based on the maximum likelihood ratio is described in this correspondence. The algorithm detects low-intensity targets and tracks their paths. The charged-coupled device (CCD) mosaic sensor collects photons from the targets and the sky background. The algorithm computes the noise statistics, simulates the target pattern, correlates the received data with the target patterns, ... View full abstract»

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  • On an Extended Fisher Criterion for Feature Selection

    Publication Year: 1981, Page(s):611 - 614
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (661 KB)

    This correspondence considers the extraction of features as a task of linear transformation of an initial pattern space into a new space, optimal with respect to discriminating the data. A solution of the feature extraction problem is given for two multivariate normal distributed pattern classes using an extended Fisher criterion as the distance measure. The introduced distance measure consists of... View full abstract»

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  • [Advertisement]

    Publication Year: 1981, Page(s):615 - 616
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  • List of Contributors

    Publication Year: 1981, Page(s): nil2
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  • [Front cover]

    Publication Year: 1981, Page(s): c2
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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
David A. Forsyth
University of Illinois
e-mail: daf@illinois.edu