Object recognition: fundamentals and case studies
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London [u.a.]
Springer
2002
|
| Schriftenreihe: | Advances in pattern recognition
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 350 S. Ill., graph. Darst. |
| ISBN: | 1852333987 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1804128935358758912 |
|---|---|
| adam_text | M. BENNAMOUN AND G.J. MAMIE OBJECT RECOGNITION FUNDAMENTALS AND CASE
STUDIES WITH 133 FIGURES SPRINGER CONTENTS PART A * INTRODUCTION AND
ACQUISITION SYSTEMS 1 1. INTRODUCTION 3 1.1 WHAT IS COMPUTER VISION? 3
1.2 BACKGROUND AND HISTORY 4 1.3 CLASSIFICATION OF EXISTING VISION
SYSTEMS 5 1.3.1 MARR S THEORY 6 1.3.2 MODEL-BASED OBJECT RECOGNITION 8
1.4 PROBLEM FORMULATION 9 1.4.1 MATHEMATICAL FORMULATION 9 1.5 WHY IS
AUTOMATIC OBJECT RECOGNITION A DIFFICULT PROBLEM? .. 12 1.6 MOTIVATIONS
AND SIGNIFICANCE 13 1.6.1 INDUSTRY 13 1.6.2 COMMUNITY 14 1.7 A 2-D
SYSTEM OR A 3-D SYSTEM? 14 1.8 SPECIFICATIONS/THEMES OF INTEREST IN
OBJECT RECOGNITION 15 1.9 ACQUISITION SYSTEMS 17 1.9.1 INTENSITY IMAGES
18 1.9.2 RANGE IMAGING TECHNOLOGIES 19 1.9.3 MISCELLANEOUS MODALITIES 27
1.10 TAXONOMY 27 2. STEREO MATCHING AND RECONSTRUCTION OF A DEPTH MAP 29
2.1 FUNDAMENTALS OF STEREO VISION 29 2.1.1 STEREO VISION PARADIGM 30
2.1.2 IMAGE MATCHING 31 2.1.3 MATCHING PROBLEMS 31 2.2 REVIEW OF
EXISTING TECHNIQUES 33 2.3 AREA-BASED TECHNIQUES 33 2.3.1 SIMPLE
MATCHING MEASURES 34 2.3.2 VALIDATION TECHNIQUES 35 2.3.3 HIERARCHICAL
METHODS 38 2.3.4 ADAPTIVE WINDOW TECHNIQUES 38 X CONTENTS 2.3.5 SPARSE
POINT MATCHING 41 2.3.6 DENSE MATCHING 42 2.3.7 SYMMETRIC MULTI-WINDOW
TECHNIQUE 45 2.3.8 UNMANNED GROUND VEHICLE IMPLEMENTATION 46 2.3.9
MULTIPLE BASELINE TECHNIQUES 47 2.3.10 LEAST SQUARES MATCHING 50 2.4
TRANSFORM-BASED TECHNIQUES 53 2.4.1 SIGN REPRESENTATION 54 2.4.2
NON-PARAMETRIC TECHNIQUES 58 2.5 SYMBOLIC FEATURE-BASED TECHNIQUES 61
2.5.1 ZERO CROSSING MATCHING 62 2.5.2 EDGE MATCHING 62 2.5.3 PATCH
MATCHING 73 2.5.4 RELATIONAL MATCHING 74 2.6 HYBRID TECHNIQUES 74 2.6.1
CROSS CORRELATION COMBINED WITH EDGE INFORMATION .. 74 2.7 PHASE-BASED
TECHNIQUES 78 2.8 COMBINING INDEPENDENT MEASUREMENTS 79 2.9 RELAXATION
TECHNIQUES 80 2.9.1 COOPERATIVE ALGORITHM 80 2.9.2 RELAXATION LABELLING
81 2.10 DYNAMIC PROGRAMMING 81 2.10.1 VITERBI ALGORITHM 82 2.10.2 INTRA-
AND INTER-SCANLINE SEARCH 83 2.10.3 DISPARITY SPACE IMAGE 86 2.11 OBJECT
SPACE TECHNIQUES 87 2.11.1 COMBINING MATCHING AND SURFACE RECONSTRUCTION
87 2.11.2 OBJECT SPACE MODELS 88 2.12 EXISTING MATCHING CONSTRAINTS AND
DIAGNOSTICS 89 2.12.1 MATCHING CONSTRAINTS 89 2.12.2 MATCHING
DIAGNOSTICS 92 2.12.3 DISCUSSION 92 2.13 CONCLUSIONS 95 PART A * SUMMARY
97 PART B * DATABASE CREATION AND MODELLING FOR 3-D OBJECT RECOGNITION
99 3. 3-D OBJECT CREATION FOR RECOGNITION 101 3.1 PRELIMINARIES OF 3-D
REGISTRATION 102 3.2 REGISTRATION PARADIGM 103 3.2.1 GENERAL
SPECIFICATIONS 104 3.3 CHRONOLOGICAL LITERATURE REVIEW 104 3.4
FUNDAMENTAL TECHNIQUES 107 CONTENTS XI 3.4.1 REGISTRATION WITH POINT
CORRESPONDENCES 108 3.4.2 REGISTRATION WITHOUT CORRESPONDENCES 112 3.5
UNCERTAINTY IN 3-D REGISTRATION 117 3.5.1 WEIGHTED CORRESPONDENCES 118
3.5.2 A BETTER APPROACH 119 3.6 SIMULTANEOUS MULTIPLE VIEW REGISTRATION
122 3.6.1 SIMPLE APPROACHES 122 3.6.2 RIGID BODY MODELLING 122 3.6.3
MULTIPLE VIEW CHEN AND MEDIONI 123 3.7 VIEW INTEGRATION AND SURFACE
RECONSTRUCTION 125 3.7.1 INTEGRATION VERSUS RECONSTRUCTION 126 3.7.2
VOLUMETRIC INTEGRATION METHODS 127 3.7.3 VOLUMETRIC RECONSTRUCTION 130
3.7.4 GEOMETRIC INTEGRATION METHODS 135 3.7.5 GEOMETRIC RECONSTRUCTION
140 3.8 REGISTRATION - CASE STUDY 143 3.8.1 NOTATION AND TERMINOLOGY 144
3.8.2 PROBLEM REFORMULATION 147 3.8.3 ITERATIVE ALGORITHM TO SOLVE FOR R
149 3.8.4 RESULTS 155 3.8.5 CONCLUSIONS 157 3.9 SURFACE RECONSTRUCTION
SUMMARY 158 4. OBJECT REPRESENTATION AND FEATURE MATCHING 161 4.1
PRELIMINARIES 161 4.2 OBJECT-CENTRED REPRESENTATIONS 163 4.2.1 BOUNDARY
AND CURVE-BASED REPRESENTATIONS 163 4.2.2 AXIAL DESCRIPTIONS 169 4.2.3
SURFACE DESCRIPTIONS 170 4.2.4 VOLUMETRIC DESCRIPTIONS 177 4.3
VIEWER-CENTRED REPRESENTATIONS 180 4.3.1 ASPECT GRAPHS 180 4.3.2
SILHOUETTES 180 4.3.3 PRINCIPAL COMPONENT ANALYSIS 183 4.3.4
MISCELLANEOUS TECHNIQUES 184 4.4 REPRESENTATION CONCLUSIONS 186 4.5
MATCHING 186 4.5.1 HYPOTHESISE AND TEST 187 4.5.2 RELATIONAL STRUCTURES
188 4.5.3 POSE CLUSTERING 190 4.5.4 GEOMETRIC HASHING 190 4.5.5
INTERPRETATION TREES 191 4.5.6 REGISTRATION AND DISTANCE TRANSFORMS 194
4.6 MATCHING CONCLUSIONS 194 XII CONTENTS PART B - SUMMARY 195 PART C -
VISION SYSTEMS - CASE STUDIES 197 5. OPTICAL CHARACTER RECOGNITION 199
5.1 EXAMPLES OF EXISTING SYSTEMS 200 5.1.1 PROTOTYPE EXTRACTION AND
ADAPTIVE OCR 201 5.1.2 DIRECT GRAYSCALE EXTRACTION OF FEATURES FOR
CHARACTER RECOGNITION 201 5.2 OPTICAL CHARACTER RECOGNITION SYSTEM FOR
CURSIVE SCRIPTS - A CASE STUDY 201 5.2.1 BACKGROUND 202 5.2.2 AN
OVERVIEW OF THE CASE STUDY SYSTEM 205 5.2.3 THE DOCUMENT IMAGE ANALYSIS
STEP 206 5.2.4 THE RECOGNITION-BASED SEGMENTATION STEP 213 5.2.5 THE
FEATURE EXTRACTION STAGE 214 5.2.6 RESULTS 218 5.2.7 CONCLUSIONS 220 6.
RECOGNITION BY PARTS AND PART SEGMENTATION TECHNIQUES 221 6.1 EXAMPLES
OF EXISTING VISION SYSTEMS 221 6.1.1 HYPER 222 6.1.2 THE LOGARITHMIC
COMPLEXITY MATCHING TECHNIQUE .... 223 6.2 RECOGNITION BY PARTS AND PART
SEGMENTATION - A CASE STUDY 224 6.2.1 THE EDGE DETECTION STAGE 225 6.2.2
THE PART SEGMENTATION STAGE 229 6.2.3 PART ISOLATION 233 6.2.4 THE PART
IDENTIFICATION STAGE 234 6.2.5 THE STRUCTURAL DESCRIPTION AND
RECOGNITION STAGE .... 237 6.2.6 RESULTS 240 6.2.7 DISCUSSION 249 6.2.8
CONCLUSIONS 259 7. 3-D OBJECT RECOGNITION SYSTEMS 261 7.1 EXAMPLES OF
EXISTING SYSTEMS 261 7.1.1 ACRONYM 262 7.1.2 SCERPO 263 7.1.3 3DPO 263
7.1.4 PREMIO 263 7.1.5 RECOGNITION OF MSTAR TARGETS 264 7.1.6 BAYESIAN
RECOGNITION BY PARTS IN FLIR 265 7.2 3-D FREE-FORM OBJECT RECOGNITION
USING BAYESIAN SPLINES - A CASE STUDY 265 7.2.1 PRELIMINARIES 267
CONTENTS XIII 7.2.2 BAYESIAN FORMULATION 269 7.2.3 THE RJMCMC ALGORITHM
FOR SPLINES 272 7.2.4 SIMULATED ANNEALING RJMCMC 275 7.2.5 MATCHING
SPLINES 276 7.2.6 RESULTS 279 7.2.7 CONCLUSIONS 292 PART C - SUMMARY 295
APPENDICES 297 A. VECTOR AND MATRIX ANALYSIS 299 A.I PRELIMINARIES 299
A.1.1 DETERMINANT 300 A.1.2 INVERSION 301 , A.2 DERIVATIVES AND
INTEGRALS OF MATRICES 302 A.3 VECTORS AND VECTOR ANALYSIS 302 A.4
EIGENVALUES AND EIGENVECTORS 304 A.5 QUADRATIC FORMS 304 B. PRINCIPAL
COMPONENT ANALYSIS 305 C. OPTIMISATION FUNDAMENTALS 307 C.I FUNDAMENTAL
CONCEPTS 307 C.2 LINEAR LEAST SQUARES 308 C.3 NON-LINEAR OPTIMISATION
309 C.4 DIRECT SEARCH TECHNIQUES 310 C.4.1 SIMPLEX METHOD 310 C.5
GRADIENT METHODS 311 C.5.1 NEWTON-RAPHSON TECHNIQUE 311 C.5.2
DAVIDON-FLETCHER-POWELL 312 C.6 SIMULATED ANNEALING 313 D. DIFFERENTIAL
GEOMETRY * BASIC PRINCIPLES 315 E. SPLINE THEORY 319 E.I SPLINE
DEFINITIONS 319 F. DETAILED DERIVATION OF REGISTRATION EQUATIONS 323 F.I
SIMPLIFICATION OF # R , T 323 F.2 SIMPLIFICATION OF # R 327 REFERENCES
329 INDEX 347
|
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| indexdate | 2024-07-09T18:57:09Z |
| institution | BVB |
| isbn | 1852333987 |
| language | English |
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| physical | XIII, 350 S. Ill., graph. Darst. |
| publishDate | 2002 |
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| series2 | Advances in pattern recognition |
| spelling | Bennamoun, Mohammed 1961- Verfasser (DE-588)123121795 aut Object recognition fundamentals and case studies M. Bennamoun and G. J. Mamic London [u.a.] Springer 2002 XIII, 350 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advances in pattern recognition Objekterkennung (DE-588)4314334-9 gnd rswk-swf Objekterkennung (DE-588)4314334-9 s DE-604 Mamic, George 1977- Verfasser (DE-588)123121817 aut HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009638333&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bennamoun, Mohammed 1961- Mamic, George 1977- Object recognition fundamentals and case studies Objekterkennung (DE-588)4314334-9 gnd |
| subject_GND | (DE-588)4314334-9 |
| title | Object recognition fundamentals and case studies |
| title_auth | Object recognition fundamentals and case studies |
| title_exact_search | Object recognition fundamentals and case studies |
| title_full | Object recognition fundamentals and case studies M. Bennamoun and G. J. Mamic |
| title_fullStr | Object recognition fundamentals and case studies M. Bennamoun and G. J. Mamic |
| title_full_unstemmed | Object recognition fundamentals and case studies M. Bennamoun and G. J. Mamic |
| title_short | Object recognition |
| title_sort | object recognition fundamentals and case studies |
| title_sub | fundamentals and case studies |
| topic | Objekterkennung (DE-588)4314334-9 gnd |
| topic_facet | Objekterkennung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009638333&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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