Markov random field modeling in image analysis:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
London
Springer
2009
|
| Ausgabe: | 3. ed. |
| Schriftenreihe: | Advances in Pattern Recognition
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016962420&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XXIII, 357 S. Ill., graph. Darst. 235 mm x 155 mm |
| ISBN: | 1848002785 9781848002784 |
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| 245 | 1 | 0 | |a Markov random field modeling in image analysis |c Stan Z. Li |
| 250 | |a 3. ed. | ||
| 264 | 1 | |a London |b Springer |c 2009 | |
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| 650 | 4 | |a Traitement d'images - Techniques numériques - Modèles mathématiques | |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Image processing |x Digital techniques |x Mathematical models | |
| 650 | 4 | |a Markov random fields | |
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Datensatz im Suchindex
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|---|---|
| DE-BY-TUM_katkey | 1654821 |
| DE-BY-TUM_location | 01 |
| DE-BY-TUM_media_number | 040010151169 |
| _version_ | 1821932537482575872 |
| adam_text | STAN Z. LI MARKOV RANDOM FIELD MODELING IN IMAGE ANALYSIS THIRD EDITION
SPRINGER CONTENTS FOREWORD BY ANIL *. 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 5 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 6 MRF 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 AKAIKE 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 XXIII 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- |
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| callnumber-subject | TA - General and Civil Engineering |
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| ctrlnum | (OCoLC)262720244 (DE-599)DNB988023032 |
| dewey-full | 621.36701519233 |
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| dewey-sort | 3621.36701519233 |
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| discipline | Informatik Mathematik Elektrotechnik / Elektronik / Nachrichtentechnik |
| edition | 3. ed. |
| format | Book |
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| id | DE-604.BV035155231 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T13:24:22Z |
| institution | BVB |
| isbn | 1848002785 9781848002784 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016962420 |
| oclc_num | 262720244 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-29T DE-19 DE-BY-UBM |
| owner_facet | DE-91G DE-BY-TUM DE-29T DE-19 DE-BY-UBM |
| physical | XXIII, 357 S. Ill., graph. Darst. 235 mm x 155 mm |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Springer |
| record_format | marc |
| series2 | Advances in Pattern Recognition |
| spellingShingle | Li, Stan Z. 1958- Markov random field modeling in image analysis Champs aléatoires de Markov Traitement d'images - Techniques numériques - Modèles mathématiques Mathematisches Modell Image processing Digital techniques Mathematical models Markov random fields Maschinelles Sehen (DE-588)4129594-8 gnd Markov-Zufallsfeld (DE-588)4168933-1 gnd Parameterschätzung (DE-588)4044614-1 gnd Bildverarbeitung (DE-588)4006684-8 gnd |
| subject_GND | (DE-588)4129594-8 (DE-588)4168933-1 (DE-588)4044614-1 (DE-588)4006684-8 |
| 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 | Champs aléatoires de Markov Traitement d'images - Techniques numériques - Modèles mathématiques Mathematisches Modell Image processing Digital techniques Mathematical models Markov random fields Maschinelles Sehen (DE-588)4129594-8 gnd Markov-Zufallsfeld (DE-588)4168933-1 gnd Parameterschätzung (DE-588)4044614-1 gnd Bildverarbeitung (DE-588)4006684-8 gnd |
| topic_facet | Champs aléatoires de Markov Traitement d'images - Techniques numériques - Modèles mathématiques Mathematisches Modell Image processing Digital techniques Mathematical models Markov random fields Maschinelles Sehen Markov-Zufallsfeld Parameterschätzung Bildverarbeitung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016962420&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT listanz markovrandomfieldmodelinginimageanalysis |
Inhaltsverzeichnis
Paper/Kapitel scannen lassen
Paper/Kapitel scannen lassen
Teilbibliothek Mathematik & Informatik
| Signatur: |
0102 DAT 760f 2001 A 22733(3) Lageplan |
|---|---|
| Exemplar 1 | Ausleihbar Am Standort |