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Markov Random Field Modeling in Image Analysis

  • Book
  • © 2001

Overview

Authors:
  1. Stan Z. Li

    1. Beijing Sigma Center, Microsoft Research China, Beijing, China

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  • Valuable reference for researchers
  • Covers deeply a broad range of Markov Random Field Theory

Part of the book series: Computer Science Workbench (WORKBENCH)

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Table of contents (9 chapters)

  1. Front Matter

    Pages I-XIX
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  2. Introduction

    • Stan Z. Li
    Pages 1-42

  3. Low Level MRF Models

    • Stan Z. Li
    Pages 43-80

  4. High Level MRF Models

    • Stan Z. Li
    Pages 81-118

  5. Discontinuities in MRFs

    • Stan Z. Li
    Pages 119-145

  6. Discontinuity-Adaptivity Model and Robust Estimation

    • Stan Z. Li
    Pages 147-163

  7. MRF Parameter Estimation

    • Stan Z. Li
    Pages 165-196

  8. Parameter Estimation in Optimal Object Recognition

    • Stan Z. Li
    Pages 197-224

  9. Minimization — Local Methods

    • Stan Z. Li
    Pages 225-248

  10. Minimization — Global Methods

    • Stan Z. Li
    Pages 249-285

  11. Back Matter

    Pages 287-323
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Keywords

About this book

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.

Authors and Affiliations

  • Beijing Sigma Center, Microsoft Research China, Beijing, China

    Stan Z. Li

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