Verfügbarkeit: Markov random field modeling in image analysis

Markov random field modeling in image analysis:

Gespeichert in:

Bibliographische Detailangaben
1. Verfasser:	Li, Stan Z. 1958- (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	London Springer 2009
Ausgabe:	3. ed.
Schriftenreihe:	Advances in Pattern Recognition
Schlagworte:	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
Online-Zugang:	Inhaltsverzeichnis
Beschreibung:	XXIII, 357 S. Ill., graph. Darst. 235 mm x 155 mm
ISBN:	1848002785 9781848002784

Internformat

MARC


LEADER	00000nam a2200000 c 4500
001	BV035155231
003	DE-604
005	20090513
007	t
008	081112s2009 ad\|\| \|\|\|\| 00\|\|\| eng d
015			\|a 08,N15,0013 \|2 dnb
016	7		\|a 988023032 \|2 DE-101
020			\|a 1848002785 \|c Gb. : ca. EUR 64.15 (freier Pr.), ca. sfr 104.50 (freier Pr.) \|9 1-8480-0278-5
020			\|a 9781848002784 \|c Gb. : ca. EUR 64.15 (freier Pr.), ca. sfr 104.50 (freier Pr.) \|9 978-1-84800-278-4
024	3		\|a 9781848002784
028	5	2	\|a 12203222
035			\|a (OCoLC)262720244
035			\|a (DE-599)DNB988023032
040			\|a DE-604 \|b ger \|e rakddb
041	0		\|a eng
049			\|a DE-91G \|a DE-29T \|a DE-19
050		0	\|a TA1637
082	0		\|a 621.36701519233 \|2 22
084			\|a ST 330 \|0 (DE-625)143663: \|2 rvk
084			\|a 004 \|2 sdnb
084			\|a DAT 760f \|2 stub
084			\|a MAT 607f \|2 stub
100	1		\|a Li, Stan Z. \|d 1958- \|e Verfasser \|0 (DE-588)114045003 \|4 aut
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
300			\|a XXIII, 357 S. \|b Ill., graph. Darst. \|c 235 mm x 155 mm
336			\|b txt \|2 rdacontent
337			\|b n \|2 rdamedia
338			\|b nc \|2 rdacarrier
490	0		\|a Advances in Pattern Recognition
650		4	\|a Champs aléatoires de Markov
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
650	0	7	\|a Maschinelles Sehen \|0 (DE-588)4129594-8 \|2 gnd \|9 rswk-swf
650	0	7	\|a Markov-Zufallsfeld \|0 (DE-588)4168933-1 \|2 gnd \|9 rswk-swf
650	0	7	\|a Parameterschätzung \|0 (DE-588)4044614-1 \|2 gnd \|9 rswk-swf
650	0	7	\|a Bildverarbeitung \|0 (DE-588)4006684-8 \|2 gnd \|9 rswk-swf
689	0	0	\|a Bildverarbeitung \|0 (DE-588)4006684-8 \|D s
689	0	1	\|a Parameterschätzung \|0 (DE-588)4044614-1 \|D s
689	0	2	\|a Markov-Zufallsfeld \|0 (DE-588)4168933-1 \|D s
689	0	3	\|a Maschinelles Sehen \|0 (DE-588)4129594-8 \|D s
689	0		\|8 1\p \|5 DE-604
689	1	0	\|a Bildverarbeitung \|0 (DE-588)4006684-8 \|D s
689	1	1	\|a Parameterschätzung \|0 (DE-588)4044614-1 \|D s
689	1	2	\|a Markov-Zufallsfeld \|0 (DE-588)4168933-1 \|D s
689	1		\|5 DE-604
776	0	8	\|i Erscheint auch als \|n Online-Ausgabe \|z 978-1-84800-279-1
856	4	2	\|m GBV Datenaustausch \|q application/pdf \|u 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 \|3 Inhaltsverzeichnis
999			\|a oai:aleph.bib-bvb.de:BVB01-016962420
883	1		\|8 1\p \|a cgwrk \|d 20201028 \|q DE-101 \|u https://d-nb.info/provenance/plan#cgwrk

Datensatz im Suchindex

_version_	1804138315501273088
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
adam_txt	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
any_adam_object_boolean	1
author	Li, Stan Z. 1958-
author_GND	(DE-588)114045003
author_facet	Li, Stan Z. 1958-
author_role	aut
author_sort	Li, Stan Z. 1958-
author_variant	s z l sz szl
building	Verbundindex
bvnumber	BV035155231
callnumber-first	T - Technology
callnumber-label	TA1637
callnumber-raw	TA1637
callnumber-search	TA1637
callnumber-sort	TA 41637
callnumber-subject	TA - General and Civil Engineering
classification_rvk	ST 330
classification_tum	DAT 760f MAT 607f
ctrlnum	(OCoLC)262720244 (DE-599)DNB988023032
dewey-full	621.36701519233
dewey-hundreds	600 - Technology (Applied sciences)
dewey-ones	621 - Applied physics
dewey-raw	621.36701519233
dewey-search	621.36701519233
dewey-sort	3621.36701519233
dewey-tens	620 - Engineering and allied operations
discipline	Informatik Mathematik Elektrotechnik / Elektronik / Nachrichtentechnik
discipline_str_mv	Informatik Mathematik Elektrotechnik / Elektronik / Nachrichtentechnik
edition	3. ed.
format	Book
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02732nam a2200661 c 4500</leader><controlfield tag="001">BV035155231</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20090513 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">081112s2009 ad\|\| \|\|\|\| 00\|\|\| eng d</controlfield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">08,N15,0013</subfield><subfield code="2">dnb</subfield></datafield><datafield tag="016" ind1="7" ind2=" "><subfield code="a">988023032</subfield><subfield code="2">DE-101</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1848002785</subfield><subfield code="c">Gb. : ca. EUR 64.15 (freier Pr.), ca. sfr 104.50 (freier Pr.)</subfield><subfield code="9">1-8480-0278-5</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781848002784</subfield><subfield code="c">Gb. : ca. EUR 64.15 (freier Pr.), ca. sfr 104.50 (freier Pr.)</subfield><subfield code="9">978-1-84800-278-4</subfield></datafield><datafield tag="024" ind1="3" ind2=" "><subfield code="a">9781848002784</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">12203222</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)262720244</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DNB988023032</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakddb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91G</subfield><subfield code="a">DE-29T</subfield><subfield code="a">DE-19</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TA1637</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">621.36701519233</subfield><subfield code="2">22</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 330</subfield><subfield code="0">(DE-625)143663:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">004</subfield><subfield code="2">sdnb</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 760f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">MAT 607f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Stan Z.</subfield><subfield code="d">1958-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)114045003</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Markov random field modeling in image analysis</subfield><subfield code="c">Stan Z. Li</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">3. ed.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">London</subfield><subfield code="b">Springer</subfield><subfield code="c">2009</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XXIII, 357 S.</subfield><subfield code="b">Ill., graph. Darst.</subfield><subfield code="c">235 mm x 155 mm</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Advances in Pattern Recognition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Champs aléatoires de Markov</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Traitement d'images - Techniques numériques - Modèles mathématiques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematisches Modell</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image processing</subfield><subfield code="x">Digital techniques</subfield><subfield code="x">Mathematical models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Markov random fields</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Sehen</subfield><subfield code="0">(DE-588)4129594-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Markov-Zufallsfeld</subfield><subfield code="0">(DE-588)4168933-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Parameterschätzung</subfield><subfield code="0">(DE-588)4044614-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Bildverarbeitung</subfield><subfield code="0">(DE-588)4006684-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Bildverarbeitung</subfield><subfield code="0">(DE-588)4006684-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Parameterschätzung</subfield><subfield code="0">(DE-588)4044614-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Markov-Zufallsfeld</subfield><subfield code="0">(DE-588)4168933-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="3"><subfield code="a">Maschinelles Sehen</subfield><subfield code="0">(DE-588)4129594-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="1" ind2="0"><subfield code="a">Bildverarbeitung</subfield><subfield code="0">(DE-588)4006684-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="1"><subfield code="a">Parameterschätzung</subfield><subfield code="0">(DE-588)4044614-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="2"><subfield code="a">Markov-Zufallsfeld</subfield><subfield code="0">(DE-588)4168933-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-1-84800-279-1</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">GBV Datenaustausch</subfield><subfield code="q">application/pdf</subfield><subfield code="u">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</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-016962420</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield></record></collection>
id	DE-604.BV035155231
illustrated	Illustrated
index_date	2024-07-02T22:48:21Z
indexdate	2024-07-09T21:26:14Z
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
spelling	Li, Stan Z. 1958- Verfasser (DE-588)114045003 aut Markov random field modeling in image analysis Stan Z. Li 3. ed. London Springer 2009 XXIII, 357 S. Ill., graph. Darst. 235 mm x 155 mm txt rdacontent n rdamedia nc rdacarrier Advances in Pattern Recognition 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 rswk-swf Markov-Zufallsfeld (DE-588)4168933-1 gnd rswk-swf Parameterschätzung (DE-588)4044614-1 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 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 DE-604 Erscheint auch als Online-Ausgabe 978-1-84800-279-1 GBV Datenaustausch application/pdf 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 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 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_exact_search_txtP	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

Verfügbarkeit

Es ist kein Print-Exemplar vorhanden.

Fernleihe Bestellen Achtung: Nicht im THWS-Bestand! Inhaltsverzeichnis

MARC

Datensatz im Suchindex

Es ist kein Print-Exemplar vorhanden.

Ähnliche Einträge