Markov Random Field Modeling in Image Analysis / Edition 3

Hardcover (Print)
Buy New
Buy New from
Used and New from Other Sellers
Used and New from Other Sellers
from $94.07
Usually ships in 1-2 business days
(Save 27%)
Other sellers (Hardcover)
  • All (10) from $94.07   
  • New (8) from $94.07   
  • Used (2) from $103.19   


"Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. When used with optimization principles, it also enables systematic development of optimal vision algorithms. This book presents a comprehensive study on the use of MRFs for solving computer vision problems, with an 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 updated edition includes the important progress made in Markov modeling in image analysis in recent years, such as Markov modeling of images with "macro" patterns (the FRAME model, for one), Markov chain Monte Carlo (MCMC) methods, and reversible jump MCMC."--BOOK JACKET.
Read More Show Less

Editorial Reviews

From the Publisher
From the reviews of the third edition:

"Prof. Li’s book … provides a comprehensive introduction to the area of MRF in general and to its applications in image processing in specific. … is very well written with a plethora of references for the reader that wants to delve further into specific areas. … In conclusion, this book is very thorough, both in a mathematic and a descriptive manner. Anyone interested in image processing and its applications … can benefit from the variety of provided examples and its wide range of references." (Apostolos Georgakis, IAPR Newsletter, Vol. 31 (4), October, 2009)

"This book elegantly and effectively elaborates on MRF theory and related topics. Each chapter includes the problem definition, related mathematical formulation and method explanations, and very useful examples. … This is an excellent book on MRF theory for image analysis. Researchers and graduate students will find this book very useful for understanding the theory clearly." (Fatih Kurugollu, ACM Computing Reviews, November, 2009)

Read More Show Less

Product Details

Table of Contents

Preface to the Second Edition
1 Introduction 1
1.1 Visual Labeling 3
1.2 Markov Random Fields and Gibbs Distributions 8
1.3 Useful MRF Models 17
1.4 Optimization-Based Vision 30
1.5 Bayes Labeling of MRFs 35
1.6 Validation of Modeling 40
2 Low Level MRF Models 43
2.1 Observation Models 44
2.2 Image Restoration and Reconstruction 45
2.3 Edge Detection 54
2.4 Texture Synthesis and Analysis 58
2.5 Optical Flow 65
2.6 Bayesian Deformable Models 68
3 High Level MRF Models 81
3.1 Matching Under Relational Constraints 81
3.2 MRF-Based Matching 88
3.3 Optimal Matching to Multiple Overlapping Objects 104
3.4 Pose Computation 112
4 Discontinuities in MRFs 119
4.1 Smoothness, Regularization and Discontinuities 120
4.2 The Discontinuity Adaptive MRF Model 126
4.3 Modeling Roof Discontinuities 136
4.4 Experimental Results 141
5 Discontinuity-Adaptivity Model and Robust Estimation 147
5.1 The DA Prior and Robust Statistics 148
5.2 Experimental Comparison 156
6 MRF Parameter Estimation 165
6.1 Supervised Estimation with Labeled Data 166
6.2 Unsupervised Estimation with Unlabeled Data 181
6.3 Further Issues 192
7 Parameter Estimation in Optimal Object Recognition 197
7.1 Motivation 197
7.2 Theory of Parameter Estimation for Recognition 199
7.3 Application in MRF Object Recognition 210
7.4 Experiments 216
8 Minimization - Local Methods 225
8.1 Problem Categorization 225
8.2 Classical Minimization with Continuous Labels 228
8.3 Minimization with Discrete Labels 229
8.4 Constrained Minimization 239
8.5 Augmented Lagrange-Hopfield Method 244
9 Minimization - Global Methods 249
9.1 Simulated Annealing 250
9.2 Mean Field Annealing 252
9.3 Graduated Non-Convexity 255
9.4 Genetic Algorithms 261
9.5 Experimental Comparisons 269
9.6 Accelerating Computation 282
References 287
List of Notation 317
Index 319
Read More Show Less

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star


4 Star


3 Star


2 Star


1 Star


Your Rating:

Your Name: Create a Pen Name or

Barnes & Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation


  • - By submitting a review, you grant to Barnes & and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Terms of Use.
  • - Barnes & reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)