Markov Random Field Modeling in Image Analysis / Edition 3

Markov Random Field Modeling in Image Analysis / Edition 3

by Stan Z. Li
     
 

ISBN-10: 1848002785

ISBN-13: 9781848002784

Pub. Date: 03/10/2009

Publisher: Springer London

"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…  See more details below

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.

Product Details

ISBN-13:
9781848002784
Publisher:
Springer London
Publication date:
03/10/2009
Series:
Advances in Computer Vision and Pattern Recognition Series
Edition description:
3rd ed. 2009
Pages:
362
Product dimensions:
6.30(w) x 9.30(h) x 1.00(d)

Table of Contents

Foreword
Preface to the Second Edition
1Introduction1
1.1Visual Labeling3
1.2Markov Random Fields and Gibbs Distributions8
1.3Useful MRF Models17
1.4Optimization-Based Vision30
1.5Bayes Labeling of MRFs35
1.6Validation of Modeling40
2Low Level MRF Models43
2.1Observation Models44
2.2Image Restoration and Reconstruction45
2.3Edge Detection54
2.4Texture Synthesis and Analysis58
2.5Optical Flow65
2.6Bayesian Deformable Models68
3High Level MRF Models81
3.1Matching Under Relational Constraints81
3.2MRF-Based Matching88
3.3Optimal Matching to Multiple Overlapping Objects104
3.4Pose Computation112
4Discontinuities in MRFs119
4.1Smoothness, Regularization and Discontinuities120
4.2The Discontinuity Adaptive MRF Model126
4.3Modeling Roof Discontinuities136
4.4Experimental Results141
5Discontinuity-Adaptivity Model and Robust Estimation147
5.1The DA Prior and Robust Statistics148
5.2Experimental Comparison156
6MRF Parameter Estimation165
6.1Supervised Estimation with Labeled Data166
6.2Unsupervised Estimation with Unlabeled Data181
6.3Further Issues192
7Parameter Estimation in Optimal Object Recognition197
7.1Motivation197
7.2Theory of Parameter Estimation for Recognition199
7.3Application in MRF Object Recognition210
7.4Experiments216
8Minimization - Local Methods225
8.1Problem Categorization225
8.2Classical Minimization with Continuous Labels228
8.3Minimization with Discrete Labels229
8.4Constrained Minimization239
8.5Augmented Lagrange-Hopfield Method244
9Minimization - Global Methods249
9.1Simulated Annealing250
9.2Mean Field Annealing252
9.3Graduated Non-Convexity255
9.4Genetic Algorithms261
9.5Experimental Comparisons269
9.6Accelerating Computation282
References287
List of Notation317
Index319

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