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Markov Random Field Modeling in Image Analysis

  • Stan Z. Li

Part of the Computer Science Workbench book series (WORKBENCH)

Table of contents

  1. Front Matter
    Pages I-XIX
  2. Stan Z. Li
    Pages 1-42
  3. Stan Z. Li
    Pages 43-80
  4. Stan Z. Li
    Pages 81-118
  5. Stan Z. Li
    Pages 119-145
  6. Stan Z. Li
    Pages 165-196
  7. Stan Z. Li
    Pages 225-248
  8. Stan Z. Li
    Pages 249-285
  9. Back Matter
    Pages 287-323

About this book

Introduction

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. The book covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This second edition includes the most important progress in Markov modeling in image analysis in recent years such as Markov modeling of images with "macro" patterns (e.g. the FRAME model), Markov chain Monte Carlo (MCMC) methods, reversible jump MCMC. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.

Keywords

Excel Markov Random Field Markov model Optical flow Ringe algorithms calculus computer vision image analysis image processing image restoration object recognition pattern recognition statistics texture synthesis

Authors and affiliations

  • Stan Z. Li
    • 1
  1. 1.Beijing Sigma CenterMicrosoft Research ChinaBeijingChina

Bibliographic information

  • DOI https://doi.org/10.1007/978-4-431-67044-5
  • Copyright Information Springer-Verlag Tokyo 2001
  • Publisher Name Springer, Tokyo
  • eBook Packages Springer Book Archive
  • Print ISBN 978-4-431-70309-9
  • Online ISBN 978-4-431-67044-5
  • Series Print ISSN 1431-1488
  • Buy this book on publisher's site
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