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Artificial Neural Networks for Computer Vision

  • Textbook
  • © 1992

Overview

Authors:
  1. Yi-Tong Zhou

    1. HNC, Inc., San Diego, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Rama Chellappa

    1. Department of Electrical Engineering Center for Automation Research and Institute for Advanced Computer Studies, University of Maryland, College Park, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

Part of the book series: Research Notes in Neural Computing (NEURALCOMPUTING, volume 5)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xi
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  2. Introduction

    • Yi-Tong Zhou, Rama Chellappa
    Pages 1-5

  3. Computational Neural Networks

    • Yi-Tong Zhou, Rama Chellappa
    Pages 6-14

  4. Static Stereo

    • Yi-Tong Zhou, Rama Chellappa
    Pages 15-43

  5. Motion Stereo—Lateral Motion

    • Yi-Tong Zhou, Rama Chellappa
    Pages 44-62

  6. Motion Stereo—Longitudinal Motion

    • Yi-Tong Zhou, Rama Chellappa
    Pages 63-82

  7. Computation of Optical Flow

    • Yi-Tong Zhou, Rama Chellappa
    Pages 83-121

  8. Image Restoration

    • Yi-Tong Zhou, Rama Chellappa
    Pages 122-146

  9. Conclusions and Future Research

    • Yi-Tong Zhou, Rama Chellappa
    Pages 147-150

  10. Back Matter

    Pages 151-170
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Keywords

About this book

This monograph is an outgrowth of the authors' recent research on the de­ velopment of algorithms for several low-level vision problems using artificial neural networks. Specific problems considered are static and motion stereo, computation of optical flow, and deblurring an image. From a mathematical point of view, these inverse problems are ill-posed according to Hadamard. Researchers in computer vision have taken the "regularization" approach to these problems, where one comes up with an appropriate energy or cost function and finds a minimum. Additional constraints such as smoothness, integrability of surfaces, and preservation of discontinuities are added to the cost function explicitly or implicitly. Depending on the nature of the inver­ sion to be performed and the constraints, the cost function could exhibit several minima. Optimization of such nonconvex functions can be quite involved. Although progress has been made in making techniques such as simulated annealing computationally more reasonable, it is our view that one can often find satisfactory solutions using deterministic optimization algorithms.

Authors and Affiliations

  • HNC, Inc., San Diego, USA

    Yi-Tong Zhou

  • Department of Electrical Engineering Center for Automation Research and Institute for Advanced Computer Studies, University of Maryland, College Park, USA

    Rama Chellappa

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