Markov random field modeling in computer vision:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | German |
| Veröffentlicht: |
Tokyo u.a.
Springer
1995
|
| Schriftenreihe: | Computer science workbench
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVI, 264 S. Ill., graph. Darst. |
| ISBN: | 3540701451 0387701451 4431701451 |
Internformat
MARC
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| 100 | 1 | |a Li, S. Z. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Markov random field modeling in computer vision |c S. Z. Li |
| 264 | 1 | |a Tokyo u.a. |b Springer |c 1995 | |
| 300 | |a XVI, 264 S. |b Ill., graph. Darst. | ||
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| 338 | |b nc |2 rdacarrier | ||
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Datensatz im Suchindex
| _version_ | 1807682215386021888 |
|---|---|
| adam_text |
CONTENTS
FOREWORD
BY
ANIL
K.
JAIN
IX
PREFACE
XI
1
INTRODUCTION
1
1.1
VISUAL
LABELING
.
3
1.1.1
SITES
AND
LABELS
.
3
1.1.2
THE
LABELING
PROBLEM
.
4
1.1.3
LABELING
PROBLEMS
IN
VISION
.
6
1.1.4
LABELING
WITH
CONTEXTUAL
CONSTRAINTS
.
7
1.2
MARKOV
RANDOM
FIELDS
AND
GIBBS
DISTRIBUTIONS
.
8
1.2.1
NEIGHBORHOOD
SYSTEM
AND
CLIQUES
.
8
1.2.2 MARKOV
RANDOM
FIELDS
.
11
1.2.3
GIBBS
RANDOM
FIELDS
.
12
1.2.4
MARKOV-GIBBS
EQUIVALENCE
.
14
1.2.5
NORMALIZED
AND
CANONICAL
FORMS
.
16
1.3
USEFUL
MRF
MODELS
.
17
1.3.1
AUTO-MODELS
.
17
1.3.2
MULTI-LEVEL
LOGISTIC
MODEL
.
19
1.3.3
THE
SMOOTHNESS
PRIOR
.
21
1.3.4
HIERARCHICAL
GRF
MODEL
.
23
1.4
OPTIMIZATION-BASED
VISION
.
24
1.4.1
RESEARCH
ISSUES
.
25
1.4.2
ROLE
OF
ENERGY
FUNCTIONS
.
26
1.4.3
FORMULATION
OF
OBJECTIVE
FUNCTIONS
.
27
1.4.4
OPTIMALITY
CRITERIA
.
29
1.5
BAYES
LABELING
OF
MRFS
.
30
1.5.1
BAYES
ESTIMATION
.
31
1.5.2
MAP-MRF
LABELING
.
32
1.5.3
REGULARIZATION
.
33
1.5.4
SUMMARY
OF
MAP-MRF
APPROACH
.
34
XIV
CONTENTS
2
LOW
LEVEL
MRF
MODELS
37
2.1
OBSERVATION
MODELS
.
38
2.2
IMAGE
RESTORATION
AND
RECONSTRUCTION
.
39
2.2.1
MRF
PRIORS
FOR
IMAGE
SURFACES
.
39
2.2.2
PIECEWISE
CONSTANT
RESTORATION
.
42
2.2.3
PIECEWISE
CONTINUOUS
RESTORATION
.
43
2.2.4
SURFACE
RECONSTRUCTION
.
46
2.3
EDGE
DETECTION
.
48
2.3.1
EDGE
LABELING
USING
LINE
PROCESS
.
49
2.3.2
FORBIDDEN
EDGE
PATTERNS
.
51
2.4
TEXTURE
SYNTHESIS
AND
ANALYSIS
.
52
2.4.1
MRF
TEXTURE
MODELING
.
53
2.4.2
TEXTURE
SEGMENTATION
.
56
2.5
OPTICAL
FLOW
.
59
2.5.1
VARIATIONAL
APPROACH
.
59
2.5.2
FLOW
DISCONTINUITIES
.
61
3
DISCONTINUITIES
IN
MRFS
63
3.1
SMOOTHNESS,
REGULARIZATION
AND
DISCONTINUITIES
.
64
3.1.1
REGULARIZATION
AND
DISCONTINUITIES
.
65
3.1.2
OTHER
REGULARIZATION
MODELS
.
69
3.2
THE
DISCONTINUITY
ADAPTIVE
MRF
MODEL
.
69
3.2.1
DEFINING
THE
DA
MODEL
.
70
3.2.2
RELATIONS
WITH
PREVIOUS
MODELS
.
74
3.2.3
DISCRETE
DATA
AND
2D
CASES
.
76
3.2.4
SOLUTION
STABILITY
.
77
3.3
COMPUTATION
OF
DA
SOLUTIONS
.
78
3.3.1
SOLVING
THE
EULER
EQUATION
.
78
3.3.2
EXPERIMENTAL
RESULTS
.
80
3.4
CONCLUSION
.
81
4
DISCONTINUITY-ADAPTIVITY
MODEL
AND
ROBUST
ESTIMATION
83
4.1
THE
DA
PRIOR
AND
ROBUST
STATISTICS
.
84
4.1.1
ROBUST
M
ESTIMATOR
.
85
4.1.2
PROBLEMS
WITH
M
ESTIMATOR
.
87
4.1.3
REDEFINITION
OF
M
ESTIMATOR
.
88
4.1.4
AM
ESTIMATOR
.
89
4.1.5
CONVEX
DA
AND
M-ESTIMATION
MODELS
.
90
4.2
EXPERIMENTAL
COMPARISON
.
92
4.2.1
LOCATION
ESTIMATION
.
92
4.2.2
ROTATION
ANGLE
ESTIMATION
.
96
CONTENTS
XV
5
HIGH
LEVEL
MRF
MODELS
101
5.1
MATCHING
UNDER
RELATIONAL
CONSTRAINTS
.
101
5.1.1
RELATIONAL
STRUCTURE
REPRESENTATION
.
102
5.1.2
WORK
IN
RELATIONAL
MATCHING
.
106
5.2
MRF-BASED
MATCHING
.
108
5.2.1
POSTERIOR
PROBABILITY
AND
ENERGY
.
109
5.2.2
MATCHING
TO
MULTIPLE
OBJECTS
.
111
5.2.3
EXPERIMENTS
.
113
5.2.4
EXTENSIONS
.
122
5.3
POSE
COMPUTATION
.
124
5.3.1
POSE
CLUSTERING
AND
ESTIMATION
.
124
5.3.2
SIMULTANEOUS
MATCHING
AND
POSE
.
127
5.3.3
DISCUSSION
.
130
6
MRF
PARAMETER
ESTIMATION
131
6.1
SUPERVISED
ESTIMATION
WITH
LABELED
DATA
.
133
6.1.1
MAXIMUM
LIKELIHOOD
.
133
6.1.2
PSEUDO-LIKELIHOOD
.
135
6.1.3
CODING
METHOD
.
136
6.1.4
MEAN
FIELD
APPROXIMATIONS
.
137
6.1.5
LEAST
SQUARES
FIT
.
139
6.2
UNSUPERVISED
ESTIMATION
WITH
UNLABELED
DATA
.
143
6.2.1
SIMULTANEOUS
RESTORATION
AND
ESTIMATION
.
144
6.2.2
SIMULTANEOUS
SEGMENTATION
AND
ESTIMATION
.
145
6.2.3
EXPECTATION-MAXIMIZATION
.
150
6.2.4
CROSS
VALIDATION
.
152
6.3
FUERTHER
ISSUES
.
153
6.3.1
ESTIMATING
THE
NUMBER
OF
MRFS
.
153
6.3.2
REDUCTION
OF
NONZERO
PARAMETERS
.
155
7
PARAMETER
ESTIMATION
IN
OPTIMAL
OBJECT
RECOGNITION
157
7.1
MOTIVATION
.
157
7.2
THEORY
OF
PARAMETER
ESTIMATION
FOR
RECOGNITION
.
159
7.2.1
OPTIMIZATION-BASED
OBJECT
RECOGNITION
.
160
7.2.2
CRITERIA
FOR
PARAMETER
ESTIMATION
.
161
7.2.3
LINEAR
CLASSIFICATION
FUNCTION
.
164
7.2.4
A
NON-PARAMETRIC
LEARNING
ALGORITHM
.
167
7.2.5
REDUCING
SEARCH
SPACE
.
169
7.3
APPLICATION
IN
MRF
OBJECT
RECOGNITION
.
170
7.3.1
POSTERIOR
ENERGY
.
170
7.3.2
ENERGY
IN
LINEAR
FORM
.
171
7.3.3
HOW
MINIMAL
CONFIGURATION
CHANGES
.
172
7.3.4
PARAMETRIC
ESTIMATION
UNDER
GAUSSIAN
NOISE
.
173
7.4
EXPERIMENTS
.
176
7.4.1
RECOGNITION
OF
LINE
PATTERNS
.
176
XVI
CONTENTS
7.4.2
RECOGNITION
OF
CURVED
OBJECTS
.
181
7.5
CONCLUSION
.
182
8
MINIMIZATION
-
LOCAL
METHODS
185
8.1
CLASSICAL
MINIMIZATION
WITH
CONTINUOUS
LABELS
.
187
8.2
MINIMIZATION
WITH
DISCRETE
LABELS
.
189
8.2.1
ITERATED
CONDITIONAL
MODES
.
189
8.2.2
RELAXATION
LABELING
.
190
8.2.3
HIGHEST
CONFIDENCE
FIRST
.
195
8.2.4
DYNAMIC
PROGRAMMING
.
197
8.3
CONSTRAINED
MINIMIZATION
.
199
8.3.1
PENALTY
FUNCTIONS
.
200
8.3.2
LAGRANGE
MULTIPLIERS
.
201
8.3.3
HOPFIELD
METHOD
.
202
8.3.4
RL
USING
LAGRANGE-HOPFIELD
METHOD
.
204
9
MINIMIZATION
-
GLOBAL
METHODS
207
9.1
SIMULATED
ANNEALING
.
208
9.1.1
RANDOM
SAMPLING
ALGORITHMS
.
209
9.1.2
ANNEALING
.
210
9.2
MEAN
FIELD
ANNEALING
.
211
9.3
GRADUATED
NON-CONVEXITY
.
214
9.3.1
ANNEALING
LABELING
FOR
MAP-MRF
MATCHING
.
219
9.4 GENETIC
ALGORITHMS
.
220
9.5
EXPERIMENTAL
COMPARISON
.
222
9.6
ACCELERATING
COMPUTATION
.
228
9.6.1
MULTI-RESOLUTION
METHODS
.
228
9.6.2
USE
OF
HEURISTICS
.
229
9.7
MODEL
DEBUGGING
.
230
REFERENCES
231
LIST
OF
NOTATION
259
INDEX
261 |
| any_adam_object | 1 |
| author | Li, S. Z. |
| author_facet | Li, S. Z. |
| author_role | aut |
| author_sort | Li, S. Z. |
| author_variant | s z l sz szl |
| building | Verbundindex |
| bvnumber | BV010445112 |
| classification_rvk | ST 330 |
| ctrlnum | (OCoLC)246786984 (DE-599)BVBBV010445112 |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV010445112 |
| illustrated | Illustrated |
| indexdate | 2024-08-18T00:14:59Z |
| institution | BVB |
| isbn | 3540701451 0387701451 4431701451 |
| language | German |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-006961658 |
| oclc_num | 246786984 |
| open_access_boolean | |
| owner | DE-739 DE-29T DE-83 |
| owner_facet | DE-739 DE-29T DE-83 |
| physical | XVI, 264 S. Ill., graph. Darst. |
| publishDate | 1995 |
| publishDateSearch | 1995 |
| publishDateSort | 1995 |
| publisher | Springer |
| record_format | marc |
| series2 | Computer science workbench |
| spelling | Li, S. Z. Verfasser aut Markov random field modeling in computer vision S. Z. Li Tokyo u.a. Springer 1995 XVI, 264 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Computer science workbench Markov-Prozess (DE-588)4134948-9 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 gnd rswk-swf Markov-Zufallsfeld (DE-588)4168933-1 gnd rswk-swf Zufälliges Feld (DE-588)4191094-1 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 s Zufälliges Feld (DE-588)4191094-1 s DE-604 Markov-Zufallsfeld (DE-588)4168933-1 s Mathematisches Modell (DE-588)4114528-8 s Markov-Prozess (DE-588)4134948-9 s 1\p DE-604 Bildverarbeitung (DE-588)4006684-8 s 2\p DE-604 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=006961658&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 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Li, S. Z. Markov random field modeling in computer vision Markov-Prozess (DE-588)4134948-9 gnd Mathematisches Modell (DE-588)4114528-8 gnd Bildverarbeitung (DE-588)4006684-8 gnd Markov-Zufallsfeld (DE-588)4168933-1 gnd Zufälliges Feld (DE-588)4191094-1 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
| subject_GND | (DE-588)4134948-9 (DE-588)4114528-8 (DE-588)4006684-8 (DE-588)4168933-1 (DE-588)4191094-1 (DE-588)4129594-8 |
| title | Markov random field modeling in computer vision |
| title_auth | Markov random field modeling in computer vision |
| title_exact_search | Markov random field modeling in computer vision |
| title_full | Markov random field modeling in computer vision S. Z. Li |
| title_fullStr | Markov random field modeling in computer vision S. Z. Li |
| title_full_unstemmed | Markov random field modeling in computer vision S. Z. Li |
| title_short | Markov random field modeling in computer vision |
| title_sort | markov random field modeling in computer vision |
| topic | Markov-Prozess (DE-588)4134948-9 gnd Mathematisches Modell (DE-588)4114528-8 gnd Bildverarbeitung (DE-588)4006684-8 gnd Markov-Zufallsfeld (DE-588)4168933-1 gnd Zufälliges Feld (DE-588)4191094-1 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
| topic_facet | Markov-Prozess Mathematisches Modell Bildverarbeitung Markov-Zufallsfeld Zufälliges Feld Maschinelles Sehen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=006961658&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT lisz markovrandomfieldmodelingincomputervision |