Computer vision: detection, recognition and reconstruction
Saved in:
| Other Authors: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg
Springer
2010
|
| Series: | Studies in computational intelligence
285 |
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Item Description: | Literaturangaben. - Zusätzliches Online-Angebot unter www.springer.com |
| Physical Description: | XX, 350 S. Ill., graph. Darst. |
| ISBN: | 9783642128479 9783642128486 |
Staff View
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Record in the Search Index
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CONTENTS 1 IS HUMAN VISION ANY GOOD? 1 JAN J. KOENDERINK 1.1
INTRODUCTION 1 1.1.1 WHAT IS "HUMAN VISION"? 1 1.1.2 HOW IS HUMAN VISION
IMPLEMENTED? 2 1.2 FRAMEWORKS 5 1.2.1 THE SPATIAL FRAMEWORK 6 1.2.2 THE
PHOTOMETRIC FRAMEWORK 9 1.3 A CASE STUDY: "SHAPE FROM SHADING" 13 1.3.1
THE SO CALLED "SHAPE FROM SHADING PROBLEM" . 13 1.3.2 SETTING UP THE
PROBLEM 15 1.3.3 THE LOCAL SHAPE FROM SHADING PROBLEM 18 1.4 FINAL
REMARKS 24 REFERENCES 25 2 KNOWING A GOOD FEATURE WHEN YOU SEE IT:
GROUND TRUTH AND METHODOLOGY TO EVALUATE LOCAL FEATURES FOR RECOGNITION
27 ANDREA VEDALDI, HAIBIN LING, STEFANO SOATTO 2.1 INTRODUCTION 27 2.2
EMPIRICAL STUDIES OF LOCAL FEATURES 29 2.2.1 SOME NOMENCLATURE 30 2.3
CONSTRUCTING A RIGOROUS GROUND TRUTH 30 2.3.1 MODELING THE DETECTOR 30
2.3.2 MODELING CORRESPONDENCES 33 2.3.3 GROUND-TRUTH CORRESPONDENCES 34
2.3.4 COMPARING GROUND-TRUTH AND REAL-WORLD CORRESPONDENCES 36 2.3.5 THE
DATA 37 2.4 LEARNING TO COMPARE INVARIANT FEATURES 40 BIBLIOGRAFISCHE
INFORMATIONEN HTTP://D-NB.INFO/1001251768 DIGITALISIERT DURCH XII
CONTENTS 2.4.1 WIDE BASELINE MOTION STATISTICS 41 2.4.2 LEARNING TO RANK
MATCHES 41 2.4.3 LEARNING TO ACCEPT MATCHES 43 2.5 DISCUSSION 44
APPENDIX 1: CALCULATIONS 44 APPENDIX 2: ALGORITHMS 45 REFERENCES 48 3
DYNAMIC GRAPH CUTS AND THEIR APPLICATIONS IN COMPUTER VISION 51 PUSHMEET
KOHLI, PHILIP H.S. TORR 3.1 INTRODUCTION 51 3.1.1 MARKOV AND CONDITIONAL
RANDOM FIELDS 52 3.2 GRAPH CUTS FOR ENERGY MINIMIZATION 54 3.2.1 THE
ST-MINCUT PROBLEM 54 3.2.2 FORMULATING THE MAX-FLOW PROBLEM 55 3.2.3
AUGMENTING PATHS, RESIDUAL GRAPHS 56 3.2.4 MINIMIZING FUNCTIONS USING
GRAPH CUTS 56 3.3 MINIMIZING DYNAMIC ENERGY FUNCTIONS USING DYNAMIC
GRAPH CUTS 58 3.3.1 DYNAMIC COMPUTATION 58 3.3.2 ENERGY AND GRAPH
REPARAMETERIZATION 60 3.4 RECYCLING COMPUTATION 61 3.4.1 UPDATING
RESIDUAL GRAPHS 62 3.4.2 COMPUTATIONAL COMPLEXITY OF UPDATE OPERATIONS
64 3.5 IMPROVING PERFORMANCE BY RECYCLING SEARCH TREES 65 3.5.1 REUSING
SEARCH TREES 65 3.5.2 TREE RECYCLING FOR DYNAMIC GRAPH CUTS 66 3.6
DYNAMIC IMAGE SEGMENTATION 67 3.6.1 CRFS FOR IMAGE SEGMENTATION 68 3.6.2
IMAGE SEGMENTATION IN VIDEOS 70 3.6.3 EXPERIMENTAL RESULTS 70 3.6.4
REUSING FLOW VS. REUSING SEARCH TREES 73 3.7 SIMULTANEOUS SEGMENTATION
AND POSE ESTIMATION OF HUMANS 74 3.7.1 POSE SPECIFIC CRF FOR IMAGE
SEGMENTATION 78 3.7. CONTENTS XIII 3.8.3 MIN-MARGINALS AND FLOW
POTENTIALS 99 3.8.4 EXTENSION TO MULTIPLE LABELS 100 3.8.5 MINIMIZING
ENERGY FUNCTION PROJECTIONS USING DYNAMIC GRAPH CUTS 101 3.8.6
COMPUTATIONAL COMPLEXITY AND EXPERIMENTAL EVALUATION 101 3.8.7
APPLICATIONS OF MIN-MARGINALS 102 REFERENCES 104 4 DISCRIMINATIVE
GRAPHICAL MODELS FOR CONTEXT-BASED CLASSIFICATION 109 SANJIV KUMAR 4.1
CONTEXTUAL DEPENDENCIES IN IMAGES 109 4.1.1 THE NATURE OF CONTEXTUAL
INTERACTIONS 110 4.2 MARKOV RANDOM FIELD (MRF) 112 4.3 CONDITIONAL
RANDOM FIELD (CRF) 113 4.3.1 ASSOCIATION POTENTIAL , 115 4.3.2
INTERACTION POTENTIAL 117 4.4 PARAMETER LEARNING AND INFERENCE 119 4.4.1
MAXIMUM LIKELIHOOD PARAMETER LEARNING 119 4.4.2 INFERENCE 121 4.5
EXTENSIONS 121 4.5.1 MULTICLASS CRF 122 4.5.2 HIERARCHICAL CRF 123 4.6
APPLICATIONS 127 4.6.1 MAN-MADE STRUCTURE DETECTION 127 4.6.2 IMAGE
CLASSIFICATION AND CONTEXTUAL OBJECT DETECTION 129 4.7 RELATED WORK AND
FURTHER READINGS 133 REFERENCES 133 5 FROM THE SUBSPACE METHODS TO THE
MUTUAL SUBSPACE METHOD 135 KEN-ICHI MAEDA 5.1 INTRODUCTION 135 5.2 THE
SUBSPACE METHODS 139 5.2.1 A BRIEF HISTORY OF THE SUBSPACE METHODS 139
5.2.2 BASIC IDEA 139 5.2.3 SUBSPACE CONSTRUCTION 140 5. XIV CONTENTS 6
WHAT, WHERE AND WHO? TELLING THE STORY OF AN IMAGE BY ACTIVITY
CLASSIFICATION, SCENE RECOGNITION AND OBJECT CATEGORIZATION 157 LI
FEI-FEI, LI-JIA LI 6.1 INTRODUCTION AND MOTIVATION 157 6.2 OVERALL
APPROACH 159 6.3 LITERATURE REVIEW 160 6.4 THE INTEGRATIVE MODEL 160
6.4.1 LABELING AN UNKNOWN IMAGE 163 6.5 LEARNING THE MODEL 164 6.6
SYSTEM IMPLEMENTATION 165 6.7 EXPERIMENTS AND RESULTS 166 6.7.1 DATASET
166 6.7.2 EXPERIMENTAL SETUP 166 6.7.3 RESULTS 169 6.8 CONCLUSION 169
REFERENCES 170 7 SEMANTIC TEXTON FORESTS 173 MATTHEW JOHNSON, JAMIE
SHOTTON 7.1 INTRODUCTION 173 7.2 RELATED WORK 174 7.3 RANDOMIZED
DECISION FORESTS 175 7.3.1 TRAINING THE FOREST 176 7.3.2 EXPERIMENTS 180
7.4 IMAGE CATEGORIZATION 183 7.4.1 TREE HISTOGRAMS AND PYRAMID MATCHING
184 7.4.2 CATEGORIZATION RESULTS 186 7.5 SEMANTIC SEGMENTATION 188 7.6
SOFT CLASSIFICATION OF PIXELS 189 7.7 IMAGE-LEVEL SEMANTIC CONSTRAINTS
191 7.7.1 CATEGORIZATION RESULTS 192 7.7.2 THE IMAGE LEVEL PRIOR 192 7.8
COMPOSITIONAL CONSTRAINTS 194 7.9 EXPERIMENTS 194 7.9.1 MSRC21 DATASET
196 7.9.2 VOC 2007 SEGMENTATION DATASET 200 7.10 DISCUSSION 200
REFERENCES 201 8 MULTI-VIEW OBJECT CATEGORIZATION AND POSE ESTIMATIO
CONTENTS XV 8.3.1 OVERVIEW 210 8.3.2 CANONICAL PARTS AND LINKAGE
STRUCTURE 211 8.4 BUILDING THE MODEL 212 8.4.1 EXTRACT FEATURES 213
8.4.2 FORM PARTS 213 8.4.3 FIND CANONICAL PARTS CANDIDATES 214 8.4.4
CREATE THE MODEL 216 8.5 VIEW SYNTHESIS 217 8.5.1 REPRESENTING AN UNSEEN
VIEW 217 8.6 RECOGNIZING OBJECT CLASS IN UNSEEN VIEWS 219 8.6.1 EXTRACT
FEATURES AND GET PART CANDIDATES 220 8.6.2 RECOGNITION PROCEDURE: FIRST
STEP 221 8.6.3 RECOGNITION PROCEDURE: SECOND STEP 222 8.7 EXPERIMENTS
AND RESULTS 222 8.7.1 EXPERIMENT I: COMPARISON WITH THOMAS ET AL. [63]
222 8.7.2 EXPERIMENT II: DETECTION AND POSE ESTIMATION RESULTS ON THE
DATASET IN [54] 223 8.7.3 EXPERIMENT III: DETECTION AND POSE ESTIMATION
RESULTS ON THE DATASET IN [55] 227 8.8 CONCLUSION 227 REFERENCES 228 9 A
VISION-BASED REMOTE CONTROL 233 BJOERN STENGER, THOMAS WOODLEY, ROBERTO
CIPOLLA 9.1 INTRODUCTION 233 9.2 PRIOR WORK 235 9.2.1 HAND TRACKING FOR
HUMAN COMPUTER INTERFACES . 235 9.2.2 COMMERCIAL GESTURE INTERFACE
SYSTEMS 238 9.2.3 VISUAL TRACKING OF A SINGLE OBJECT 239 9.3 TRACKING
WITH MULTIPLE OBSERVERS 240 9.3.1 OBSERVATION MODELS 240 9.3.2
EVALUATING SINGLE OBSERVERS 243 9.3.3 EVALUATING MULTIPLE OBSERVERS 245
9.3.4 PARALLEL EVALUATION 246 9.3.5 CASCADED EVALUATION 247 9.3.6
DYNAMIC MODEL DISCUSSION 247 9.3. XVI CONTENTS 9.5 SUMMARY AND
CONCLUSION 257 REFERENCES 258 10 MULTI-VIEW MULTI-OBJECT DETECTION AND
TRACKING 263 MURTAZA TAJ, ANDREA CAVALLARO 10.1 INTRODUCTION 263 10.2
PROBLEM FORMULATION 265 10.3 CALIBRATION AND FUSION 266 10.3.1
SINGLE-LEVEL HOMOGRAPHY 266 10.3.2 MULTI-LEVEL HOMOGRAPHY 268 10.4
TRACK-FIRST APPROACHES 269 10.4.1 INDEPENDENT TRACKING 271 10.4.2
COLLABORATIVE TRACKING 271 10.5 FUSE-FIRST APPROACHES 272 10.5.1
DETECTION-BASED TRACKING 273 10.5.2 TRACK-BEFORE-DETECT 276 10.6
CONCLUSIONS 278 REFERENCES 278 11 SHAPE FROM PHOTOGRAPHS: A MULTI-VIEW
STEREO PIPELINE 281 CARLOS HERNANDEZ, GEORGE VOGIATZIS 11.1 INTRODUCTION
281 11.2 MULTI-VIEW STEREO PIPELINE: FROM PHOTOGRAPHS TO 3D MODELS 284
11.3 COMPUTING PHOTO-CONSISTENCY FROM A SET OF CALIBRATED PHOTOGRAPHS
285 11.3.1 NORMALIZED CROSS CORRELATION FOR DEPTH-MAP COMPUTATION 287
11.3.2 DEPTH MAP ESTIMATION 288 11.3.3 PHOTO-CONSISTENCY 3D MAP FROM A
SET OF DEPTH-MAPS 292 11.4 EXTRACTING A 3D SURFACE FROM A 3D MAP OF
PHOTO-CONSISTENCY 292 11.4.1 MULTI-VIEW STEREO USING MULTI-RESOLUTION
GRAPH-CUTS 292 11.4.2 DISCONTINUITY COST FROM A SET OF DEPTH-MAPS 294
11.4.3 GRAPH STRUCTURE 295 11.4.4 LABELING COST FROM A SET OF DEPTH-MAPS
295 11.4.5 PROBABILISTIC FUSION OF DEPTH SENSORS 296 11.4. CONTENTS XVII
11.5.3 DIGITIZING WORKS OF ART 304 11.6 DISCUSSION 307 REFERENCES 309 12
PRACTICAL 3D RECONSTRUCTION BASED ON PHOTOMETRIC STEREO 313 GEORGE
VOGIATZIS, CARLOS HERNANDEZ 12.1 INTRODUCTION 313 12.2 PHOTOMETRIC
STEREO WITH COLOURED LIGHT 314 12.2.1 CLASSIC THREE-SOURCE PHOTOMETRIC
STEREO 315 12.2.2 MULTI-SPECTRAL SOURCES AND SENSORS 316 12.2.3
CALIBRATION 317 12.2.4 COMPARISON WITH PHOTOMETRIC STEREO 317 12.2.5 THE
PROBLEM OF SHADOWS 318 12.2.6 FACIAL CAPTURE EXPERIMENTS 325 12.2.7
RELATED WORK 328 12.3 MULTI-VIEW PHOTOMETRIC STEREO 330 12.3.1 RELATED
WORK 331 12.3.2 ALGORITHM 333 12.3.3 EXPERIMENTS 338 REFERENCES 342
INDEX 347 |
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| spellingShingle | Computer vision detection, recognition and reconstruction Studies in computational intelligence Bildrekonstruktion (DE-588)4145435-2 gnd Bilderkennung (DE-588)4264283-8 gnd Registrierung Bildverarbeitung (DE-588)4778903-7 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
| subject_GND | (DE-588)4145435-2 (DE-588)4264283-8 (DE-588)4778903-7 (DE-588)4129594-8 (DE-588)4143413-4 |
| title | Computer vision detection, recognition and reconstruction |
| title_auth | Computer vision detection, recognition and reconstruction |
| title_exact_search | Computer vision detection, recognition and reconstruction |
| title_full | Computer vision detection, recognition and reconstruction Roberto Cipolla ... (ed.) |
| title_fullStr | Computer vision detection, recognition and reconstruction Roberto Cipolla ... (ed.) |
| title_full_unstemmed | Computer vision detection, recognition and reconstruction Roberto Cipolla ... (ed.) |
| title_short | Computer vision |
| title_sort | computer vision detection recognition and reconstruction |
| title_sub | detection, recognition and reconstruction |
| topic | Bildrekonstruktion (DE-588)4145435-2 gnd Bilderkennung (DE-588)4264283-8 gnd Registrierung Bildverarbeitung (DE-588)4778903-7 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
| topic_facet | Bildrekonstruktion Bilderkennung Registrierung Bildverarbeitung Maschinelles Sehen Aufsatzsammlung |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3449985&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=020339883&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT cipollaroberto computervisiondetectionrecognitionandreconstruction |