Markov Random Field Modeling in Image Analysis / Edition 3

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Overview

"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.
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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)

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Product Details

Table of Contents

Foreword
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
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