Computational intelligence paradigms in advanced pattern classification:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2012
|
| Schriftenreihe: | Studies in computational intelligence
386 |
| Schlagwörter: | |
| Links: | http://deposit.dnb.de/cgi-bin/dokserv?id=3866339&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=024831446&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XI, 197 S. Ill., graph. Darst. |
| ISBN: | 9783642240485 3642240488 |
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Datensatz im Suchindex
| _version_ | 1819380855665065984 |
|---|---|
| adam_text | IMAGE 1
CONTENTS
CHAPTER 1 RECENT ADVANCES IN PATTERN CLASSIFICATION 1
MAREK R. OGIELA, LAKHMI C. JAIN 1 N E W DIRECTIONS IN PATTERN
CLASSIFICATION 1
REFERENCES 4
CHAPTER 2
NEURAL NETWORKS FOR HANDWRITING RECOGNITION 5
MARCUS LIWICKI, ALEX GRAVES, HORST BUNKE 1 INTRODUCTION 5
1.1 STATE-OF-THE-ART 6
1.2 CONTRIBUTION 7
2 DATA PROCESSING 8
2.1 GENERAL PROCESSING STEPS 9
2.2 O U R ONLINE SYSTEM 10
2.3 O U R OFFLINE SYSTEM 12
3 NEURAL NETWORK BASED RECOGNITION 12
3.1 RECURRENT NEURAL NETWORKS (RNNS) 12
3.2 LONG SHORT-TERM M E M O R Y ( L S T M ) 13
3.3 BIDIRECTIONAL RECURRENT NEURAL NETWORKS 16
3.4 CONNECTIONIST TEMPORAL CLASSIFICATION (CTC) 16
3.5 MULTIDIMENSIONAL RECURRENT NEURAL NETWORKS 17
3.6 HIERARCHICAL SUBSAMPLING RECURRENT NEURAL NETWORKS 18
4 EXPERIMENTS 18
4.1 COMPARISON WITH H M M S ON THE I A M DATABASES 18
4.2 RECOGNITION PERFORMANCE O F M D L S T M ON CONTEST DATA 2 0
5 CONCLUSION 21
REFERENCES 21
CHAPTER 3
MOVING OBJECT DETECTION FROM MOBILE PLATFORMS USING STEREO DATA
REGISTRATION 25
ANGEL D. SAPPA, DAVID GERONIMO, FADI DORNAIKA, MOHAMMAD ROUHANI, ANTONIO
M. LOPEZ 1 INTRODUCTION 2 5
2 RELATED W O R K 2 6
HTTP://D-NB.INFO/1014250048
IMAGE 2
VIII C O N T E N T S
3 PROPOSED APPROACH 2 8
3.1 SYSTEM SETUP 2 9
3.2 FEATURE DETECTION AND TRACKING 2 9
3.3 ROBUST REGISTRATION 31
3.4 FRAME SUBTRACTION 31
4 EXPERIMENTAL RESULTS 3 4
5 CONCLUSIONS 3 5
REFERENCES 35
CHAPTER 4
PATTERN CLASSIFICATIONS IN COGNITIVE INFORMATICS 39
LIDIA OGIELA 1 INTRODUCTION 3 9
2 SEMANTIC ANALYSIS STAGES 4 0
3 SEMANTIC ANALYSIS VS. COGNITIVE INFORMATICS 4 3
4 EXAMPLE O F A COGNITIVE U B I A S SYSTEM 4 5
5 CONCLUSIONS 5 2
REFERENCES 5 2
CHAPTER 5
OPTIMAL DIFFERENTIAL FILTER ON HEXAGONAL LATTICE 59
SUGURU SAITO, MASAYUKI NAKAJIAMA, TETSUO SHIMA 1 INTRODUCTION 5 9
2 PRELIMINARIES 6 0
3 LEAST INCONSISTENT IMAGE 6 0
4 POINT SPREAD FUNCTION 6 4
5 CONDITION FOR GRADIENT FILTER 6 7
6 NUMERICAL OPTIMIZATION 6 8
7 THEORETICAL EVALUATION 7 0
7.1 SIGNAL-TO-NOISE RATIO 7 0
7.2 LOCALIZATION 7 3
8 EXPERIMENTAL EVALUATION 7 4
8.1 CONSTRUCTION O F ARTIFICIAL IMAGES 7 4
8.2 DETECTION O F GRADIENT INTENSITY AND ORIENTATION 7 6
8.3 OVERINGTON S METHOD O F ORIENTATION DETECTION 7 6
8.4 RELATIONSHIP BETWEEN DERIVED FILTER AND STAUNTON FILTER 7 8
8.5 EXPERIMENT AND RESULTS 8 0
9 DISCUSSION 81
10 SUMMARY 8 3
REFERENCES 8 6
IMAGE 3
CONTENTS IX
CHAPTER 6
GRAPH IMAGE LANGUAGE TECHNIQUES SUPPORTING ADVANCED CLASSIFICATION AND
COGNITIVE INTERPRETATION OF CT CORONARY VESSEL VISUALIZATIONS 89
MIROSTAW TRZUPEK 1 INTRODUCTION 8 9
2 T H E CLASSIFICATION PROBLEM 9 2
3 STAGES IN THE ANALYSIS O F C T IMAGES UNDER A STRUCTURAL APPROACH
UTILISING GRAPH TECHNIQUES 9 3
4 PARSING LANGUAGES GENERATED BY GRAPH GRAMMARS 9 5
5 PICTURE GRAMMARS IN CLASSIFICATION AND SEMANTIC INTERPRETATION O F 3 D
CORONARY VESSELS VISUALISATIONS 9 6
5.1 CHARACTERISTICS O F THE IMAGE DATA 9 6
5.2 PRELIMINARY ANALYSIS O F 3 D CORONARY VASCULARISATION
RECONSTRUCTIONS 9 6
5.3 GRAPH-BASED LINGUISTIC FORMALISMS IN SPATIAL MODELLING OF CORONARY
VESSELS 9 8
5.4 DETECTING LESIONS AND CONSTRUCTING THE SYNTACTIC ANALYSER 103
5.5 SELECTED RESULTS 104
6 CONCLUSIONS CONCERNING THE ADVANCED CLASSIFICATION AND COGNITIVE
INTERPRETATION O F C T CORONARY VESSEL VISUALIZATIONS 108
REFERENCES 110
CHAPTER 7
A GRAPH MATCHING APPROACH TO SYMMETRY DETECTION AND ANALYSIS 113 MICHAEL
CHERTOK AND YOSI KELLER 1 INTRODUCTION 113
2 SYMMETRIES AND THEIR PROPERTIES 115
2.1 ROTATIONAL SYMMETRY 115
2.2 REFLECTIONAL SYMMETRY 116
2.3 INTERRELATIONS BETWEEN ROTATIONAL AND REFLECTIONAL SYMMETRIES 117
2.4 DISCUSSION 117
3 PREVIOUS W O R K 117
3.1 PREVIOUS W O R K IN SYMMETRY DETECTION AND ANALYSIS 118
3.2 LOCAL FEATURES 121
3.3 SPECTRAL MATCHING O F SETS O F POINTS IN R N 122
4 SPECTRAL SYMMETRY ANALYSIS 123
4.1 SPECTRAL SYMMETRY ANALYSIS O F SETS IN R 123
4.1.1 PERFECT SYMMETRY AND SPECTRAL DEGENERACY 124
4.2 SPECTRAL SYMMETRY ANALYSIS O F IMAGES 124
4.2.1 IMAGE REPRESENTATION BY LOCAL FEATURES 125
4.2.2 SYMMETRY CATEGORIZATION AND PRUNING 125
4.2.3 COMPUTING THE GEOMETRICAL PROPERTIES O F THE SYMMETRY 126 5
EXPERIMENTAL RESULTS 127
5.1 SYMMETRY ANALYSIS O F IMAGES 128
5.2 STATISTICAL ACCURACY ANALYSIS 134
IMAGE 4
X C O N T E N T S
5.3 ANALYSIS O F THREE-DIMENSIONAL SYMMETRY 136
5.4 IMPLEMENTATION ISSUES 137
5.5 ADDITIONAL RESULTS 140
6 CONCLUSIONS 140
REFERENCES 142
CHAPTER 8
PATTERN CLASSIFICATION METHODS FOR ANALYSIS AND VISUALIZATION OF BRAIN
PERFUSION CT MAPS 145
TOMASZ HACHAJ 1 INTRODUCTION 145
2 INTERPRETATION O F PERFUSION M A P S - LONG AND SHORT T I M E
PROGNOSIS 148 3 IMAGE PROCESSING AND ABNORMALITY DETECTION 150
4 IMAGE REGISTRATION 153
4.1 AFFINE REGISTRATION 154
4.2 FFD REGISTRATION 154
4.3 THIRION S D E M O N S ALGORITHM 154
4.4 COMPARISON O F REGISTRATION ALGORITHMS 155
5 CLASSIFICATION O F DETECTED ABNORMALITIES 158
6 SYSTEM VALIDATION AND RESULTS 160
7 DATA VISUALIZATION - AUGMENTED REALITY ENVIRONMENT 162
7.1 AUGMENTED REALITY ENVIRONMENT 163
7.2 REAL TIME RENDERING O F 3 D DATA 164
7.3 AUGMENTED DESKTOP - SYSTEM PERFORMANCE TEST 164
8 SUMMARY 167
REFERENCES 168
CHAPTER 9
INFERENCE OF CO-OCCURRING CLASSES: MULTI-CLASS AND MULTI-LABEL
CLASSIFICATION 171
TAL SOBOL-SHIKLER 1 INTRODUCTION 171
2 APPLICATIONS 172
3 T H E CLASSIFICATION PROCESS 173
4 DATA AND ANNOTATION 175
5 CLASSIFICATION APPROACHES 177
5.1 BINARY CLASSIFICATION 177
5.2 MULTI-CLASS CLASSIFICATION 177
5.3 MULTI-LABEL CLASSIFICATION 178
6 MULTI-CLASS CLASSIFICATION 179
6.1 MULTIPLE BINARY CLASSIFIERS 180
6.1.1 ONE-AGAINST-ALL CLASSIFICATION 180
6.1.2 ONE-AGAINST-ONE (PAIR-WISE) CLASSIFICATION 180
6.1.3 COMBINING BINARY CLASSIFIERS 181
IMAGE 5
CONTENTS X I
6.2 DIRECT MULTI-CLASS CLASSIFICATION 181
6.3 ASSOCIATIVE CLASSIFICATION 182
7 MULTI-LABEL CLASSIFICATION 182
7.1 SEMI-SUPERVISED (ANNOTATION) METHODS 186
8 INFERENCE O F CO-OCCURRING AFFECTIVE STATES FROM NON-VERBAL SPEECH 186
9 SUMMARY 193
REFERENCES 193
AUTHOR INDEX 199
|
| any_adam_object | 1 |
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| dewey-search | 006.42 |
| dewey-sort | 16.42 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Medizin |
| format | Book |
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| indexdate | 2024-12-20T16:06:29Z |
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| isbn | 9783642240485 3642240488 |
| language | English |
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| physical | XI, 197 S. Ill., graph. Darst. |
| publishDate | 2012 |
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| publisher | Springer |
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| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spellingShingle | Ogiela, Marek R. 1968- Computational intelligence paradigms in advanced pattern classification Studies in computational intelligence Mustererkennung (DE-588)4040936-3 gnd Automatische Klassifikation (DE-588)4120957-6 gnd Soft Computing (DE-588)4455833-8 gnd |
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| title | Computational intelligence paradigms in advanced pattern classification |
| title_auth | Computational intelligence paradigms in advanced pattern classification |
| title_exact_search | Computational intelligence paradigms in advanced pattern classification |
| title_full | Computational intelligence paradigms in advanced pattern classification Marek R. Ogiela ... (eds.) |
| title_fullStr | Computational intelligence paradigms in advanced pattern classification Marek R. Ogiela ... (eds.) |
| title_full_unstemmed | Computational intelligence paradigms in advanced pattern classification Marek R. Ogiela ... (eds.) |
| title_short | Computational intelligence paradigms in advanced pattern classification |
| title_sort | computational intelligence paradigms in advanced pattern classification |
| topic | Mustererkennung (DE-588)4040936-3 gnd Automatische Klassifikation (DE-588)4120957-6 gnd Soft Computing (DE-588)4455833-8 gnd |
| topic_facet | Mustererkennung Automatische Klassifikation Soft Computing Aufsatzsammlung |
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