Markov random field modeling in image analysis:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London
Springer
2010
|
| Ausgabe: | 3. rev. ed., Softcover reprint of hardcover 3. ed. 2009 |
| Schriftenreihe: | Advances in pattern recognition
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Digest format paperback |
| Beschreibung: | XXIII, 357 S. Ill., graph. Darst. |
Internformat
MARC
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| 245 | 1 | 0 | |a Markov random field modeling in image analysis |c Stan Z. Li |
| 250 | |a 3. rev. ed., Softcover reprint of hardcover 3. ed. 2009 | ||
| 264 | 1 | |a London |b Springer |c 2010 | |
| 300 | |a XXIII, 357 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Advances in pattern recognition | |
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Datensatz im Suchindex
| _version_ | 1804149368808275968 |
|---|---|
| adam_text | Contents
Foreword by Anil
К.
Jain
ix
Foreword by Rama Chellappa
xi
Preface to the Third Edition
xiii
Preface to the Second Edition
xv
Preface to the First Edition
xvii
1
Introduction
1
1.1
Labeling for Image Analysis
................... 3
1.1.1
Sites and Labels
..................... 3
1.1.2
The Labeling Problem
.................. 4
1.1.3
Labeling Problems in Image Analysis
.......... 5
1.1.4
Labeling with Contextual Constraints
......... 7
1.2
Optimization-Based Approach
.................. 8
1.2.1
Research Issues
...................... 9
1.2.2
Role of Energy Functions
................ 10
1.2.3
Formulation of Objective Functions
.......... 11
1.2.4
Optimality Criteria
.................... 12
1.3
The MAP-MRF Framework
................... 13
1.3.1
Bayes
Estimation
..................... 13
1.3.2
MAP-MRF Labeling
................... 15
1.3.3
Regularization
...................... 16
1.3.4
Summary of the MAP-MRF Approach
......... 17
1.4
Validation of Modeling
...................... 18
2
Mathematical MRF Models
21
2.1
Markov Random Fields
and Gibbs Distributions
..................... 21
2.1.1
Neighborhood System and Cliques
........... 21
2.1.2
Markov Random Fields
................. 24
2.1.3
Gibbs Random Fields
.................. 26
XIX
xx
Contents
2.1.4
Markov-Gibbs Equivalence
............... 28
2.1.5
Normalized and Canonical Forms
............ 29
2.2
Auto-models
........................... 30
2.3
Multi-level Logistic Model
.................... 32
2.4
The Smoothness Prior
...................... 34
2.5
Hierarchical GRF Model
..................... 37
2.6
The FRAME Model
....................... 37
2.7
Multiresolution MRF Modeling
................. 40
2.8
Conditional Random Fields
................... 43
2.9
Discriminative Random Fields
.................. 44
2.10
Strong MRF Model
........................ 45
2.11
/C-MRF and Nakagami-MRF Models
.............. 46
2.12
Graphical Models: MRF s versus
Bayesian Networks
........................ 47
3
Low-Level MRF Models
49
3.1
Observation Models
....................... 50
3.2
Image Restoration and Reconstruction
............. 51
3.2.1
MRF Priors for Image Surfaces
............. 51
3.2.2
Piecewise Constant Restoration
............. 54
3.2.3
Piecewise Continuous Restoration
........... 56
3.2.4
Surface Reconstruction
.................. 58
3.3
Edge Detection
.......................... 60
3.3.1
Edge Labeling Using Line Process
........... 61
3.3.2
Forbidden Edge Patterns
................ 63
3.4
Texture Synthesis and Analysis
................. 65
3.4.1
MRF Texture Modeling
................. 65
3.4.2
Texture Segmentation
.................. 69
3.5
Optical Flow
........................... 71
3.5.1
Variational Approach
.................. 71
3.5.2
Flow Discontinuities
................... 73
3.6
Stereo Vision
........................... 74
3.7
Spatio-temporal Models
..................... 76
3.8
Bayesian Deformable Models
.................. 78
3.8.1
Formulation of EigenSnake
............... 80
3.8.2
Experiments
....................... 86
4
High-Level MRF Models
91
4.1
Matching under Relational Constraints
............ 91
4.1.1
Relational Structure Representation
.......... 92
4.1.2
Work in Relational Matching
.............. 96
4.2
Feature-Based Matching
..................... 98
4.2.1
Posterior Probability and Energy
............ 99
4.2.2
Matching to Multiple Objects
............. 101
4.2.3
Extensions
........................ 103
Contents
XXI
4.2.4 Experiments ....................... 105
4.3 Optimal
Matching to
Multiple
Overlapping Objects
....................... 113
4.3.1
Formulation of MAP-MRF Estimation
......... 113
4.3.2
Computational Issues
.................. 117
4.4
Pose Computation
........................ 121
4.4.1
Pose Clustering and Estimation
............. 121
4.4.2
Simultaneous Matching and Pose Estimation
..... 124
4.4.3
Discussion
......................... 127
4.5
Face Detection and Recognition
................. 127
Discontinuities in MRF s
129
5.1
Smoothness, Regularization,
and Discontinuities
........................ 130
5.1.1
Regularization and Discontinuities
........... 131
5.1.2
Other Regularization Models
.............. 135
5.2
The Discontinuity Adaptive MRF Model
........... 136
5.2.1
Defining the DA Model
................ . 136
5.2.2
Relations with Previous Models
............. 141
5.2.3
Discrete Data and 2D Cases
............... 142
5.2.4
Solution Stability
..................... 143
5.2.5
Computational Issues
.................. 144
5.3
Total Variation Models
...................... 146
5.3.1
Total Variation Norm
................. . 147
5.3.2
TV Models
........................ 147
5.3.3
Multichannel TV
..................... 150
5.4
Modeling Roof Discontinuities
.................. 151
5.4.1
Roof-Edge Model
..................... 152
5.4.2
MAP-MRF Solution
................... 154
5.4.3
Computational Issues
.................. 155
5.5
Experimental Results
...................... . 156
5.5.1
Step-Edge-Preserving Smoothing
............ 156
5.5.2
Roof-Edge-Preserving Smoothing
............ 157
MRP Model with Robust Statistics
161
6.1
The DA Prior and Robust Statistics
............. . 162
6.1.1
Robust M-Estimator
................... 163
6.1.2
Problems with M-Estimator
............... 165
6.1.3
Redefinition of M-Estimator
............... 166
6.1.4
AM-Estimator
...................... 167
6.1.5
Convex Priors for DA and M-Estimation
........ 168
6.1.6
Half-Quadratic Minimization
.............. 170
6.2
Experimental Comparison
................... 173
6.2.1
Location Estimation
................... 173
6.2.2
Rotation Angle Estimation
............... 177
xxii Contents
7 MRF Parameter
Estimation
183
7.1
Supervised Estimation with Labeled Data
........... 184
7.1.1
Maximum Likelihood
................... 184
7.1.2
Pseudo-likelihood
..................... 188
7.1.3
Coding Method
..................... 188
7.1.4
Mean Field Approximations
............... 190
7.1.5
Least Squares Fit
..................... 191
7.1.6
Markov Chain Monte Carlo Methods
.......... 194
7.1.7
Learning in the FRAME Model
............. 198
7.2
Unsupervised Estimation
with Unlabeled Data
....................... 199
7.2.1
Simultaneous Restoration and Estimation
....... 200
7.2.2
Simultaneous Segmentation and Estimation
..... 202
7.2.3
Expectation-Maximization
................ 206
7.2.4
Cross Validation
..................... 208
7.3
Estimating the Number of MRF s
................ 210
7.3.1
Akaiké
Information Criterion (AIC)
.......... 211
7.3.2
Reversible Jump MCMC
................. 211
7.4
Reduction of Nonzero Parameters
................ 213
8
Parameter Estimation in Optimal Object Recognition
215
8.1
Motivation
............................ 215
8.2
Theory of Parameter Estimation
for Recognition
.......................... 217
8.2.1
Optimization-Based Object Recognition
........ 218
8.2.2
Criteria for Parameter Estimation
........... 219
8.2.3
Linear Classification Function
.............. 222
8.2.4
A Nonparametric Learning Algorithm
......... 225
8.2.5
Reducing Search Space
.................. 227
8.3
Application in MRF Object Recognition
............ 228
8.3.1
Posterior Energy
..................... 228
8.3.2
Energy in Linear Form
.................. 229
8.3.3
How the Minimal Configuration Changes
....... 230
8.3.4
Parametric Estimation under Gaussian Noise
..... 232
8.4
Experiments
........................... 234
8.4.1
Recognition of Line Patterns
.............. 234
8.4.2
Recognition of Curved Objects
............. 238
8.4.3
Convergence
....................... 240
8.5
Conclusion
............................ 241
9
Minimization
-
Local Methods
243
9.1
Problem Categorization
..................... 243
9.2
Classical Minimization
with Continuous Labels
..................... 246
9.3
Minimization with Discrete Labels
............... 247
Contents
9.3.1
Iterated Conditional Modes
............... 247
9.3.2
Relaxation Labeling
................... 248
9.3.3
Belief Propagation
.................... 253
9.3.4
Convex Relaxation
.................... 255
9.3.5
Highest Confidence First
................. 258
9.3.6
Dynamic Programming
................. 260
9.4
Constrained Minimization
.................... 262
9.4.1
Penalty Functions
.................... 263
9.4.2 Lagrange
Multipliers
................... 264
9.4.3
Hopfield Method
..................... 265
9.5
Augmented Lagrange-Hopfield Method
............. 267
9.5.1
MAP-MRF Estimation as Constrained
Optimization
....................... 268
9.5.2
The ALH Method
.................... 269
10
Minimization
-
Global Methods
273
10.1
Simulated Annealing
....................... 274
10.2
Mean Field Annealing
...................... 276
10.3
Graduated Nonconvexity
..................... 279
10.3.1
GNC Algorithm
..................... 279
10.3.2
Annealing Labeling for MAP-MRF Matching
..... 284
10.4
Graph Cuts
............................ 285
10.4.1
Max-Flow
......................... 285
10.4.2
Two-Label Graph Cuts
................. 286
10.4.3
Multilabel Graph Cuts
.................. 287
10.5
Genetic Algorithms
........................ 289
10.5.1
Standard GA
....................... 290
10.5.2
Hybrid GA: Comb Algorithm
.............. 291
10.6
Experimental Comparisons
................... 297
10.6.1
Comparing Various Relaxation
Labeling Algorithms
................... 297
10.6.2
Comparing the ALH Algorithm with Others
...... 304
10.6.3
Comparing the Comb Algorithm with Others
..... 306
10.7
Accelerating Computation
.................... 310
10.7.1
Multiresolution Methods
................. 311
10.7.2
Use of Heuristics
..................... 313
References
315
List of Notation
351
Index
353
|
| any_adam_object | 1 |
| author | Li, Stan Z. 1958- |
| author_GND | (DE-588)114045003 |
| author_facet | Li, Stan Z. 1958- |
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| bvnumber | BV040337040 |
| classification_rvk | ST 330 |
| ctrlnum | (OCoLC)731008214 (DE-599)BVBBV040337040 |
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| edition | 3. rev. ed., Softcover reprint of hardcover 3. ed. 2009 |
| format | Book |
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| id | DE-604.BV040337040 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:21:55Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025191366 |
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| owner_facet | DE-739 |
| physical | XXIII, 357 S. Ill., graph. Darst. |
| publishDate | 2010 |
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| publishDateSort | 2010 |
| publisher | Springer |
| record_format | marc |
| series2 | Advances in pattern recognition |
| spelling | Li, Stan Z. 1958- Verfasser (DE-588)114045003 aut Markov random field modeling in image analysis Stan Z. Li 3. rev. ed., Softcover reprint of hardcover 3. ed. 2009 London Springer 2010 XXIII, 357 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advances in pattern recognition Digest format paperback Bildverarbeitung (DE-588)4006684-8 gnd rswk-swf Parameterschätzung (DE-588)4044614-1 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf Markov-Zufallsfeld (DE-588)4168933-1 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 s Parameterschätzung (DE-588)4044614-1 s Markov-Zufallsfeld (DE-588)4168933-1 s Maschinelles Sehen (DE-588)4129594-8 s 1\p DE-604 Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025191366&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Li, Stan Z. 1958- Markov random field modeling in image analysis Bildverarbeitung (DE-588)4006684-8 gnd Parameterschätzung (DE-588)4044614-1 gnd Maschinelles Sehen (DE-588)4129594-8 gnd Markov-Zufallsfeld (DE-588)4168933-1 gnd |
| subject_GND | (DE-588)4006684-8 (DE-588)4044614-1 (DE-588)4129594-8 (DE-588)4168933-1 |
| title | Markov random field modeling in image analysis |
| title_auth | Markov random field modeling in image analysis |
| title_exact_search | Markov random field modeling in image analysis |
| title_full | Markov random field modeling in image analysis Stan Z. Li |
| title_fullStr | Markov random field modeling in image analysis Stan Z. Li |
| title_full_unstemmed | Markov random field modeling in image analysis Stan Z. Li |
| title_short | Markov random field modeling in image analysis |
| title_sort | markov random field modeling in image analysis |
| topic | Bildverarbeitung (DE-588)4006684-8 gnd Parameterschätzung (DE-588)4044614-1 gnd Maschinelles Sehen (DE-588)4129594-8 gnd Markov-Zufallsfeld (DE-588)4168933-1 gnd |
| topic_facet | Bildverarbeitung Parameterschätzung Maschinelles Sehen Markov-Zufallsfeld |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025191366&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT listanz markovrandomfieldmodelinginimageanalysis |