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Markov Random Field Modeling in Computer Vision

  • S. Z. Li

Part of the Computer Science Workbench book series (WORKBENCH)

Table of contents

  1. Front Matter
    Pages I-XVI
  2. S. Z. Li
    Pages 1-35
  3. S. Z. Li
    Pages 37-61
  4. S. Z. Li
    Pages 63-81
  5. S. Z. Li
    Pages 101-130
  6. S. Z. Li
    Pages 131-156
  7. S. Z. Li
    Pages 185-206
  8. S. Z. Li
    Pages 207-230
  9. Back Matter
    Pages 231-264

About this book

Introduction

Markov random field (MRF) modeling provides a basis for the characterization of contextual constraints on visual interpretation and enables us to develop optimal vision algorithms systematically based on sound principles. This book presents a comprehensive study on using MRFs to solve computer vision problems, covering the following parts essential to the subject: introduction to fundamental theories, formulations of various vision models in the MRF framework, MRF parameter estimation, and optimization algorithms. Various MRF vision models are presented in a unified form, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This book is an excellent reference for researchers working in computer vision, image processing, pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in the subject.

Keywords

Markov Random Field Markov random field theory algorithms computer vision image processing image restoration pattern pattern recognition

Authors and affiliations

  • S. Z. Li
    • 1
  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore

Bibliographic information

  • DOI https://doi.org/10.1007/978-4-431-66933-3
  • Copyright Information Springer-Verlag Tokyo 1995
  • Publisher Name Springer, Tokyo
  • eBook Packages Springer Book Archive
  • Print ISBN 978-4-431-66935-7
  • Online ISBN 978-4-431-66933-3
  • Series Print ISSN 1431-1488
  • Buy this book on publisher's site
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