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Markov Random Field Modeling in Image Analysis
von: Stan Z. Li
Springer Verlag London Limited, 2009
ISBN: 9781848002791, 372 Seiten
3. Auflage
Format: PDF, OL
Preis: 71,64 EUR
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
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