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Markov Random Field Modeling in Image Analysis

:	Stan Z. Li
:	Markov Random Field Modeling in Image Analysis
:	Springer-Verlag
:	9781848002791
:	Advances in Computer Vision and Pattern Recognition
:	3
:	CHF 135.30
:

:	Anwendungs-Software
:	English

:	362
:	Wasserzeichen
:	PC/MAC/eReader/Tablet
:	PDF

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. 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 third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. 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.

	Foreword by Anil K. Jain	7
	Foreword by Rama Chellappa	9
	Preface to the Third Edition	11
	Preface to the Second Edition	12
	Preface to the First Edition	13
	Contents	15
	Introduction	20
	1.1 Labeling for Image Analysis	22
	1.2 Optimization-Based Approach	27
	1.3 The MAP-MRF Framework	32
	1.4 Validation of Modeling	37
	Mathematical MRF Models	40
	2.1 Markov Random Fields and Gibbs Distributions	40
	2.2 Auto-models	49
	2.3 Multi-level Logistic Model	51
	2.4 The Smoothness Prior	53
	2.5 Hierarchical GRF Model	56
	2.6 The FRAME Model	56
	2.7 Multiresolution MRF Modeling	59
	2.8 Conditional Random Fields	62
	2.9 Discriminative Random Fields	63
	2.10 Strong MRF Model	64
	2.11 K-MRF and Nakagami-MRF Models	65
	2.12 Graphical Models: MRF s versus Bayesian Networks	66
	Low-Level MRF Models	68
	3.1 Observation Models	69
	3.2 Image Restoration and Reconstruction	70
	3.3 Edge Detection	79
	3.4 Texture Synthesis and Analysis	84
	3.5 Optical Flow	90
	3.6 Stereo Vision	93
	3.7 Spatio-temporal Models	95
	3.8 Bayesian Deformable Models	97
	High-Level MRF Models	110
	4.1 Matching under Relational Constraints	110
	4.2 Feature-Based Matching	117
	4.3 Optimal Matching to Multiple Overlapping Objects	132
	4.4 Pose Computation	140
	4.5 Face Detection and Recognition	146
	Discontinuities in MRF s	148
	5.1 Smoothness, Regularization, and Discontinuities	149
	5.2 The Discontinuity Adaptive MRF Model	155
	5.3 Total Variation Models	165
	5.4 Modeling Roof Discontinuities	170
	5.5 Experimental Results	175
	MRF Model with Robust Statistics	179
	6.1 The DA Prior and Robust Statistics	180
	6.2 Experimental Comparison	191
	MRF Parameter Estimation	200
	7.1 Supervised Estimation with Labeled Data	201
	7.2 Unsupervised Estimation with Unlabeled Data	216
	7.3 Estimating the Number of MRF s	227
	7.4 Reduction of Nonzero Parameters	230
	Parameter Estimation in Optimal Object Recognition	232
	8.1 Motivation	232
	8.2 Theory of Parameter Estimation for Recognition	234
	8.3 Application in MRF Object Recognition	245
	8.4 Experiments	251
	8.5 Conclusion	258
	Minimization Local Methods	259
	9.1 Problem Categorization	259
	9.2 Classical Minimization with Continuous Labels	262
	9.3 Minimization with Discrete Labels	263
	9.4 Constrained Minimization	278
	9.5 Augmented Lagrange-Hopfield Method	283
	Minimization Global Methods	288
	10.1 Simulated Annealing	289
	10.2 Mean Field Annealing	291
	10.3 Graduated Nonconvexity	294
	10.4 Graph Cuts	300
	10.5 Genetic Algorithms	304
	10.6 Experimental Comparisons	312
	10.7 Accelerating Computation	325
	References	330
	List of Notation	366
	Index	368