Soft computing for image and multimedia data processing:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Heidelberg ; New York, NY ; Dordrecht ; London ; Berlin
Springer
2013
|
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | XV, 267 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9783642402548 3642402542 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1806326823824392192 |
|---|---|
| adam_text |
CONTENTS
1 INTRODUCTION 1
1.1 THE BRAIN AS AN INFORMATION PROCESSING SYSTEM 4
1.2 SYNAPTIC LEARNING 6
1.2.1 NEURONS AND SYNAPSES 6
1.2.2 TRANSMISSION OF INFORMATION 7
1.2.3 STORAGE AND PROCESSING OF INFORMATION 9
1.2.4 LONG-TERM POTENTIATION 10
1.3 NEURAL NETWORK AND HUMAN BRAIN 11
1.4 ARTIFICIAL NEURAL NETWORK 12
1.5 APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS 16
1.6 ARTIFICIAL NEURAL NETWORK MODELS 17
1.6.1 SIMPLE ARTIFICIAL NEURON 17
1.6.2 MULTILAYER PERCEPTRON 22
1.6.3 KOHONEN'S SELF ORGANIZING FEATURE MAP (SOFM) 30
1.7 FUZZY SETS AND FUZZY LOGIC 32
1.7.1 FUZZY SET-THEORETIC CONCEPTS 34
1.7.2 FUZZY SET-THEORETIC OPERATIONS 35
1.7.3 FUZZY CARDINALITY 35
1.7.4 MEASURES OF A FUZZY SET 36
1.7.5 FUZZY OPERATORS 37
1.7.6 FUZZY HOSTILITY INDEX 38
1.8 ROUGH SETS 40
1.8.1 SET-THEORETIC FORMALISM 40
1.8.2 EQUIVALENCE CLASSES 41
1.9 OPTIMIZATION PROBLEMS AND SOLUTION TECHNIQUES 42
1.10 GENETIC ALGORITHMS 43
1.10.1 BASIC PRINCIPLES AND FEATURES 44
1.10.2 ENCODING STRATEGY AND POPULATION 44
1.10.3 EVALUATION TECHNIQUE 45
1.10.4 GENETIC OPERATORS 45
1.10.5 PARAMETERS OF GA 47
XI
HTTP://D-NB.INFO/1038181860
XII CONTENTS
1.11 CLASSICAL DIFFERENTIAL EVOLUTIONARY ALGORITHM 47
1.11.1 MUTATION 48
1.11.2 CROSSOVER 48
/
1.11.3 SELECTION 49
1.12 SIMULATED ANNEALING 49
1.13 PARTICLE SWARM OPTIMIZATION 50
1.13.1 THE PSO ALGORITHM 51
1.13.2 MAIN PSO VARIANTS 52
1.14 MULTIOBJECTIVE OPTIMIZATION 54
1.15 APPROACHES TO MULTIOBJECTIVE OPTIMIZATION 55
1.16 MATHEMATICAL FORMULATION 55
1.17 MULTIOBJECTIVE GENETIC ALGORITHMS 55
1.17.1 DESIGN ISSUES OF MULTIOBJECTIVE GENETIC ALGORITHMS 56
1.18 MULTIOBJECTIVE DIFFERENTIAL EVOLUTIONARY ALGORITHMS 62
1.18.1 NON-PARETO-BASED TECHNIQUES 63
1.18.2 PARETO-BASED TECHNIQUES 63
1.18.3 MIXED TECHNIQUES 68
1.19 MULTIOBJECTIVE SIMULATED ANNEALING 69
1.20 CONCLUSIONS 70
2 TRANSFORMATION INVARIANT IMAGE RECOGNITION USING
MULTILAYER PERCEPTRON 73
2.1 INTRODUCTION 73
2.2 IMAGE RECOGNITION BY AN MLP ARCHITECTURE 74
2.2.1 IMAGE ACQUISITION PHASE 75
2.2.2 TRAINING THE MLP ARCHITECTURE 76
2.2.3 RECOGNITION OF TEST IMAGES 76
2.2.4 DETECTION OF TRANSFORMATIONS 76
2.3 RESULTS OF IMAGE RECOGNITION 76
2.3.1 RESULTS OF REAL-LIFE IMAGE RECOGNITION 78
2.4 TRANSFORMATION DETECTION BY AN MLP ARCHITECTURE 79
2.5 RESULTS OF TRANSFORMATION DETECTION 82
2.6 CONCLUSIONS 87
3 ENERGY-EFFICIENT INTELLIGENT LIGHTING CONTROL USING
A MULTILAYER PERCEPTRON 89
3.1 INTRODUCTION 89
3.2 PRINCIPLE OF LIGHTING CONTROL BY A MULTILAYER NEURAL NETWORK 90
3.3 PROPOSED METHODOLOGY 91
3.3.1 DETECTION OF BRIGHTNESS/DARKNESS
IN AN ILLUMINATED SCENE USING A TRAINED MLP
ARCHITECTURE 91
3.3.2 CONVERSION OF NETWORK OUTPUTS INTO ANALOG
VOLTAGE LEVELS TO ADJUST ARTIFICIAL LIGHTS 92
3.3.3 CONTINUOUS ACQUISITION OF ILLUMINATED SCENE
IMAGES BY A CCD CAMERA 92
CONTENTS XIII
3.4 RESULTS OF LIGHTING CONTROL BY MLP ARCHITECTURE 93
3.5 CONCLUSIONS 95
4 TARGET TRACKING USING FUZZY HOSTILITY INDUCED
SEGMENTATION OF OPTICAL FLOW FIELD 97
4.1 INTRODUCTION 97
4.2 OPTICAL FLOW FIELD TECHNIQUE 98
4.3 OPTICAL FLOW FIELD IN THE LIGHT OF FUZZY HOSTILITY INDEX 100
4.4 FUZZY HOSTILITY-BASED OPTICAL FLOW FIELD SEGMENTATION 100
4.4.1 EXTRACTION OF TIME-ORDERED IMAGE FRAMES
FROM A VIDEO SEQUENCE 101
4.4.2 COMPUTATION OF OPTICAL FLOW FIELD BETWEEN
THE FIRST TWO IMAGE FRAMES 101
4.4.3 DETERMINING OPTICAL FLOW REGIONS OF INTEREST
(ROI) USING PIXEL HOSTILITY INDEX 101
4.4.4 DETECTING A POINT OF INTEREST (POI) ON ROI
BY DETECTING MAXIMUM DENSITY OPTICAL FLOW REGIONS . 102
4.4.5 COMPUTATION OF OPTICAL FLOW IN THE
NEIGHBORHOOD OF POI IN SUBSEQUENT IMAGE FRAMES 103
4.5 RESULTS OF TARGET TRACKING WITH FUZZY HOSTILITY-BASED
SEGMENTATION OF OPTICAL FLOW FIELD 105
4.5.1 COMPUTATIONAL TIME REQUIREMENTS
OF THE PROPOSED METHOD 105
4.6 CONCLUSIONS 107
5 BINARY OBJECT EXTRACTION BY BIDIRECTIONAL SELF-ORGANIZING
NEURAL NETWORK ARCHITECTURE 109
5.1 INTRODUCTION 109
5.2 BIDIRECTIONAL SELF-ORGANIZING NEURAL NETWORK
(BDSONN) ARCHITECTURE ILL
5.2.1 NETWORK DYNAMICS 112
5.2.2 NETWORK OPERATION 113
5.2.3 BDSONN SELF-ORGANIZATION ALGORITHM 115
5.2.4 STABILIZATION OF THE NETWORK 116
5.3 IMPLEMENTATION RESULTS 118
5.3.1 BINARY OBJECT EXTRACTION WITH A SIGMOIDAL
ACTIVATION FUNCTION 118
5.3.2 BINARY OBJECT EXTRACTION WITH A BETA
ACTIVATION FUNCTION 132
5.4 CONCLUSIONS 157
6 MULTILEVEL OBJECT EXTRACTION BY BDSONN ARCHITECTURE 159
6.1 INTRODUCTION 159
6.2 RELATED WORK 160
XIV
CONTENTS
6.3 THE IMAGE SEGMENTATION/CLASSIFICATION PROBLEM 162
6.3.1 FORMAL DEFINITION 162
6.4 MULTILEVEL SIGMOIDAL (MUSIG) ACTIVATION FUNCTION 164
6.5 THRESHOLDING ASPECTS OF A MUTILEVEL SIGMOIDAL
(MUSIG) ACTIVATION FUNCTION 166
6.5.1 THRESHOLD (6P) BASED ON A BETA DISTRIBUTION
OF IMAGE INTENSITY INFORMATION 167
6.5.2 THRESHOLD (
9
XL
,
D
X2
)
BASED ON SKEWNESS
OF INTENSITY DISTRIBUTION OF IMAGE PIXEL NEIGHBORHOODS. 168
6.5.3 THRESHOLD (9^) BASED ON THE HETEROGENEITY
OF PIXEL NEIGHBORHOOD FUZZY SUBSETS 169
6.5.4 THRESHOLD (9J) BASED ON THE FUZZY CARDINALITY
ESTIMATES OF PIXEL NEIGHBORHOOD FUZZY SUBSETS 170
6.6 PRINCIPLE OF MULTILEVEL IMAGE SEGMENTATION
BY A MLSONN ARCHITECTURE 171
6.7 PRINCIPLE OF MULTILEVEL IMAGE SEGMENTATION
BY A PYRAMIDAL NEURAL NETWORK ARCHITECTURE 172
6.8 PRINCIPLE OF MULTILEVEL IMAGE SEGMENTATION
BY A BDSONN ARCHITECTURE WITH CONSENT 174
6.9 EVALUATION OF THE QUALITY OF SEGMENTED IMAGES 176
6.10 EXPERIMENTAL RESULTS 178
6.10.1 SEGMENTATION BY MLSONN ARCHITECTURE 179
6.10.2 SEGMENTATION BY PYRANET ARCHITECTURE 183
6.10.3 SEGMENTATION BY BDSONN ARCHITECTURE 186
6.11 CONCLUSIONS 194
7 COLOR OBJECT EXTRACTION BY PARALLEL BDSONN ARCHITECTURE 195
7.1 INTRODUCTION 195
7.2 RELATED WORK 197
7.3 PRINCIPLE OF COLOR IMAGE EXTRACTION AND SEGMENTATION
BY PSONN 199
7.3.1 THRESHOLDING CHARACTERISTICS OF PSONN 199
7.4 RESULTS OF COLOR IMAGE EXTRACTION AND SEGMENTATION
BY PSONN 200
7.4.1 EXTRACTION OF PURE COLOR IMAGES FROM A NOISY
BACKGROUND 200
7.4.2 SEGMENTATION OF TRUE COLOR IMAGES 203
7.5 PARALLEL BIDIRECTIONAL SELF-ORGANIZING NEURAL NETWORK
(PBDSONN) ARCHITECTURE 211
7.5.1 OPERATION OF PBDSONN ARCHITECTURE 216
7.5.2 PBDSONN SELF-ORGANIZATION ALGORITHM 218
7.6 RESULTS OF COLOR IMAGE EXTRACTION AND SEGMENTATION
BY PBDSONN 220
7.6.1 EXTRACTION OF PURE COLOR IMAGES FROM A NOISY
BACKGROUND 220
7.6.2 SEGMENTATION OF TRUE COLOR IMAGES 220
CONTENTS XV
7.7 COMPARATIVE PERFORMANCE STUDY OF PBDSONN
AND PSONN ARCHITECTURES 224
7.8 CONCLUSIONS 226
8 GRAY SCALE IMAGE EDGE DETECTION USING ROUGH SETS 229
8.1 INTRODUCTION 229
8.2 ROUGH SET CONCEPTS 231
8.2.1 ROUGHNESS OF IMAGE PIXELS 232
8.2.2 ROUGH PIXEL NEIGHBORHOOD HOSTILITY INDEX 234
8.3 CLASSICAL METHODS OF EDGE DETECTION 234
8.4 EDGE DETECTION METHODOLOGY USING PIXEL ROUGHNESS 235
8.4.1 INPUT IMAGE 235
8.4.2 DETERMINE GRANULES/EQUIVALENCE CLASSES 235
8.4.3 DETERMINE PIXEL ROUGHNESS 236
8.4.4 EXTRACT IMAGE EDGES BASED ON PIXEL ROUGHNESS 237
8.5 EXPERIMENTAL RESULTS OF EDGE DETECTION OBTAINED
USING PIXEL ROUGHNESS 237
8.6 EDGE DETECTION METHODOLOGY USING ROUGH PIXEL
HOSTILITY INDEX 242
8.6.1 DETERMINATION OF ROUGH PIXEL NEIGHBORHOOD
HOSTILITY INDEX 242
8.6.2 EXTRACTION OF IMAGE EDGES BASED ON ROUGH
PIXEL NEIGHBORHOOD HOSTILITY INDEX 243
8.7 EXPERIMENTAL RESULTS OF EDGE DETECTION USING ROUGH
PIXEL NEIGHBORHOOD HOSTILITY 243
8.8 CONCLUSIONS 248
REFERENCES 249
INDEX
265 |
| any_adam_object | 1 |
| author | Bhattacharyya, Siddhartha Maulik, Ujjwal |
| author_GND | (DE-588)143027115 |
| author_facet | Bhattacharyya, Siddhartha Maulik, Ujjwal |
| author_role | aut aut |
| author_sort | Bhattacharyya, Siddhartha |
| author_variant | s b sb u m um |
| building | Verbundindex |
| bvnumber | BV041571496 |
| classification_rvk | ST 330 |
| ctrlnum | (OCoLC)864529668 (DE-599)DNB1038181860 |
| dewey-full | 006.37 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.37 |
| dewey-search | 006.37 |
| dewey-sort | 16.37 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV041571496 |
| illustrated | Illustrated |
| indexdate | 2024-08-03T01:11:38Z |
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| isbn | 9783642402548 3642402542 |
| language | English |
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| open_access_boolean | |
| owner | DE-739 |
| owner_facet | DE-739 |
| physical | XV, 267 S. Ill., graph. Darst. 24 cm |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Springer |
| record_format | marc |
| spelling | Bhattacharyya, Siddhartha Verfasser aut Soft computing for image and multimedia data processing Siddhartha Bhattacharyya ; Ujjwal Maulik Heidelberg ; New York, NY ; Dordrecht ; London ; Berlin Springer 2013 XV, 267 S. Ill., graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Gehirn (DE-588)4019752-9 gnd rswk-swf Informationsverarbeitung (DE-588)4161678-9 gnd rswk-swf Bildsegmentierung (DE-588)4145448-0 gnd rswk-swf Objekterkennung (DE-588)4314334-9 gnd rswk-swf Merkmalsextraktion (DE-588)4314440-8 gnd rswk-swf Soft Computing (DE-588)4455833-8 gnd rswk-swf Bilderkennung (DE-588)4264283-8 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Bilderkennung (DE-588)4264283-8 s Objekterkennung (DE-588)4314334-9 s Bildsegmentierung (DE-588)4145448-0 s Merkmalsextraktion (DE-588)4314440-8 s Gehirn (DE-588)4019752-9 s Informationsverarbeitung (DE-588)4161678-9 s Neuronales Netz (DE-588)4226127-2 s Soft Computing (DE-588)4455833-8 s DE-604 Maulik, Ujjwal Verfasser (DE-588)143027115 aut X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4388515&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027016875&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bhattacharyya, Siddhartha Maulik, Ujjwal Soft computing for image and multimedia data processing Gehirn (DE-588)4019752-9 gnd Informationsverarbeitung (DE-588)4161678-9 gnd Bildsegmentierung (DE-588)4145448-0 gnd Objekterkennung (DE-588)4314334-9 gnd Merkmalsextraktion (DE-588)4314440-8 gnd Soft Computing (DE-588)4455833-8 gnd Bilderkennung (DE-588)4264283-8 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4019752-9 (DE-588)4161678-9 (DE-588)4145448-0 (DE-588)4314334-9 (DE-588)4314440-8 (DE-588)4455833-8 (DE-588)4264283-8 (DE-588)4226127-2 |
| title | Soft computing for image and multimedia data processing |
| title_auth | Soft computing for image and multimedia data processing |
| title_exact_search | Soft computing for image and multimedia data processing |
| title_full | Soft computing for image and multimedia data processing Siddhartha Bhattacharyya ; Ujjwal Maulik |
| title_fullStr | Soft computing for image and multimedia data processing Siddhartha Bhattacharyya ; Ujjwal Maulik |
| title_full_unstemmed | Soft computing for image and multimedia data processing Siddhartha Bhattacharyya ; Ujjwal Maulik |
| title_short | Soft computing for image and multimedia data processing |
| title_sort | soft computing for image and multimedia data processing |
| topic | Gehirn (DE-588)4019752-9 gnd Informationsverarbeitung (DE-588)4161678-9 gnd Bildsegmentierung (DE-588)4145448-0 gnd Objekterkennung (DE-588)4314334-9 gnd Merkmalsextraktion (DE-588)4314440-8 gnd Soft Computing (DE-588)4455833-8 gnd Bilderkennung (DE-588)4264283-8 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Gehirn Informationsverarbeitung Bildsegmentierung Objekterkennung Merkmalsextraktion Soft Computing Bilderkennung Neuronales Netz |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=4388515&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027016875&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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