Feature extraction and image processing:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam [u.a.]
Elsevier, Acad. Press
2008
|
| Ausgabe: | 2. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben |
| Beschreibung: | XV, 406 S. Ill., graph. Darst. |
| ISBN: | 0123725380 |
Internformat
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| 100 | 1 | |a Nixon, Mark S. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Feature extraction and image processing |c Mark S. Nixon ; Alberto S. Aguado |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier, Acad. Press |c 2008 | |
| 300 | |a XV, 406 S. |b Ill., graph. Darst. | ||
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| 500 | |a Literaturangaben | ||
| 650 | 4 | |a Mathematik | |
| 650 | 4 | |a Computer vision | |
| 650 | 4 | |a Computer vision |x Mathematics | |
| 650 | 4 | |a Pattern recognition systems | |
| 650 | 4 | |a Image processing |x Digital techniques | |
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Datensatz im Suchindex
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|---|---|
| adam_text | FEATURE EXTRACTION AND IMAGE PROCESSING SECOND EDITION MARK S. NIXON
ALBERTO S. AGUADO AMSTERDAM * BOSTON * HEIDELBERG * LONDON * NEW YORK *
OXFORD PARIS * SAN DIEGO * SAN FRANCISCO * SINGAPORE * SYDNEY * TOKYO
ACADEMIC PRESS IS AN IMPRINT OF ELSEVIER CONTENTS PREFACE XI 1 1 3 4 6 7
9 10 12 14 15 16 16 21 24 24 25 28 29 29 2 IMAGES, SAMPLING AND
FREQUENCY DOMAIN PROCESSING 33 2.1 OVERVIEW 33 2.2 IMAGE FORMATION 34
2.3 THE FOURIER TRANSFORM 37 2.4 THE SAMPLING CRITERION 43 2.5 THE
DISCRETE FOURIER TRANSFORM 47 2.5.1 ONE-DIMENSIONAL TRANSFORM 47 2.5.2
TWO-DIMENSIONAL TRANSFORM 49 2.6 OTHER PROPERTIES OF THE FOURIER
TRANSFORM 54 2.6.1 SHIFT INVARIANCE 54 2.6.2 ROTATION 56 2.6.3 FREQUENCY
SCALING 56 2.6.4 SUPERPOSITION (LINEARITY) 57 INTRODUCTION 1.1 1.2 1.3
1.4 1.5 1.6 1.7 1.8 OVERVIEW HUMAN AND COMPUTER VISION THE HUMAN VISION
SYSTEM 1.3.1 THE EYE 1.3.2 THE NEURAL SYSTEM 1.3.3 PROCESSING COMPUTER
VISION SYSTEMS 1.4.1 CAMERAS 1.4.2 COMPUTER INTERFACES 1.4.3 PROCESSING
AN IMAGE MATHEMATICAL SYSTEMS 1.5.1 MATHEMATICAL TOOLS 1.5.2 HELLO
MATHCAD, HELLO IMAGES! 1.5.3 HELLO MATLAB! ASSOCIATED LITERATURE 1.6.1
JOURNALS AND MAGAZINES 1.6.2 TEXTBOOKS 1.6.3 THE WEB CONCLUSIONS
REFERENCES V 2.7 TRANSFORMS OTHER THAN FOURIER 58 2.7.1 DISCRETE COSINE
TRANSFORM 58 2.7.2 DISCRETE HARTLEY TRANSFORM 59 2.7.3 INTRODUCTORY
WAVELETS: THE GABOR WAVELET 61 2.7.4 OTHER TRANSFORMS 63 2.8
APPLICATIONS USING FREQUENCY DOMAIN PROPERTIES 64 2.9 FURTHER READING 65
2.10 REFERENCES 66 BASIC IMAGE PROCESSING OPERATIONS 69 3.1 OVERVIEW 69
3.2 HISTOGRAMS 70 3.3 POINT OPERATORS 71 3.3.1 BASIC POINT OPERATIONS 71
3.3.2 HISTOGRAM NORMALIZATION 74 3.3.3 HISTOGRAM EQUALIZATION 75 3.3.4
THRESHOLDING 77 3.4 GROUP OPERATIONS 81 3.4.1 TEMPLATE CONVOLUTION 81
3.4.2 AVERAGING OPERATOR 84 3.4.3 ON DIFFERENT TEMPLATE SIZE 87 3.4.4
GAUSSIAN AVERAGING OPERATOR 88 3.5 OTHER STATISTICAL OPERATORS 90 3.5.1
MORE ON AVERAGING 90 3.5.2 MEDIAN FUETER 91 3.5.3 MODE FILTER 94 3.5.4
ANISOTROPIE DIFFUSION 96 3.5.5 FORCE FIELD TRANSFORM 101 3.5.6
COMPARISON OF STATISTICAL OPERATORS 102 3.6 MATHEMATICAL MORPHOLOGY 103
3.6.1 MORPHOLOGICAL OPERATORS 104 3.6.2 GREY-LEVEL MORPHOLOGY 107 3.6.3
GREY-LEVEL EROSION AND DILATION 108 3.6.4 MINKOWSKI OPERATORS 109 3.7
FURTHER READING 112 3.8 REFERENCES 113 LOW-LEVEL FEATURE EXTRACTION
(INCLUDING EDGE DETECTION) 115 4.1 OVERVIEW 115 4.2 FIRST ORDER EDGE
DETECTION OPERATORS 117 4.2.1 BASIC OPERATORS 117 4.2.2 ANALYSIS OF THE
BASIC OPERATORS 119 4.2.3 PREWITT EDGE DETECTION OPERATOR 121 4.2.4
SOBEL EDGE DETECTION OPERATOR 123 4.2.5 CANNY EDGE DETECTION OPERATOR
129 CONTENTS 4.3 SECOND ORDER EDGE DETECTION OPERATORS 137 4.3.1
MOTIVATION 137 4.3.2 BASIC OPERATORS: THE LAPLACIAN 137 4.3.3
MARR-HILDRETH OPERATOR 139 4.4 OTHER EDGE DETECTION OPERATORS 144 4.5
COMPARISON OF EDGE DETECTION OPERATORS 145 4.6 FURTHER READING ON EDGE
DETECTION 146 4.7 PHASE CONGRUENCY 147 4.8 LOCALIZED FEATURE EXTRACTION
152 4.8.1 DETECTING IMAGE CURVATURE (CORNER EXTRACTION) 153 4.8.1.1
DEFINITION OF CURVATURE 153 4.8.1.2 COMPUTING DIFFERENCES IN EDGE
DIRECTION 154 4.8.1.3 MEASURING CURVATURE BY CHANGES IN INTENSITY
(DIFFERENTIATION) 156 4.8.1.4 MORAVEC AND HARRIS DETECTORS 159 4.8.1.5
FURTHER READING ON CURVATURE 163 4.8.2 MODERN APPROACHES: REGION/PATCH
ANALYSIS 163 4.8.2.1 SCALE INVARIANT FEATURE TRANSFORM 163 4.8.2.2
SALIENCY 166 4.8.2.3 OTHER TECHNIQUES AND PERFORMANCE ISSUES 167 4.9
DESCRIBING IMAGE MOTION 167 4.9.1 AREA-BASED APPROACH 168 4.9.2
DIFFERENTIAL APPROACH 171 4.9.3 FURTHER READING ON OPTICAL FLOW 177 4.10
CONCLUSIONS 178 4.11 REFERENCES 178 5 FEATURE EXTRACTION BY SHAPE
MATCHIN G 183 5.1 OVERVIEW 183 5.2 THRESHOLDING AND SUBTRACTION 184 5.3
TEMPLATE MATCHING 186 5.3.1 DEFINITION 186 5.3.2 FOURIER TRANSFORM
IMPLEMENTATION 193 5.3.3 DISCUSSION OF TEMPLATE MATCHING 196 5.4 HOUGH
TRANSFORM 196 5.4.1 OVERVIEW 196 5.4.2 LINES 197 5.4.3 HOUGH TRANSFORM
FOR CIRCLES 203 5.4.4 HOUGH TRANSFORM FOR ELLIPSES 207 5.4.5 PARAMETER
SPACE DECOMPOSITION 210 5.4.5.1 PARAMETER SPACE REDUCTION FOR LINES 210
5.4.5.2 PARAMETER SPACE REDUCTION FOR CIRCLES 212 5.4.5.3 PARAMETER
SPACE REDUCTION FOR ELLIPSES 217 5.5 GENERALIZED HOUGH TRANSFORM 221
5.5.1 FORMAL DEFINITION OF THE GHT 221 5.5.2 POLAR DEFINITION 223
CONTENTS VII 6.1 6.2 6.3 6.4 6.5 6.6 6.7 OVERVIEW DEFORMABLE TEMPLATES
ACTIVE CONTOURS (SNAKES) 6.3.1 BASICS 6.3.2 THE GREEDY ALGORITHM FOR
SNAKES 6.3.3 COMPLETE (KASS) SNAKE IMPLEMENTATION 6.3.4 OTHER SNAKE
APPROACHES 6.3.5 FURTHER SNAKE DEVELOPMENTS 6.3.6 GEOMETRIE ACTIVE
CONTOURS SHAPE SKELETONIZATION 6.4.1 DISTANCE TRANSFORMS 6.4.2 SYMMETRY
FLEXIBLE SHAPE MODEIS: ACTIVE SHAPE AND ACTIVE APPEARANCE FURTHER
READING REFERENCES 5.5.3 THE GHT TECHNIQUE 224 5.5.4 INVARIANT GHT 228
5.6 OTHER EXTENSIONS TO THE HOUGH TRANSFORM 235 5.7 FURTHER READING 236
5.8 REFERENCES 237 6 FLEXIBLE SHAPE EXTRACTION (SNAKES AND OTHER
TECHNIQUES) 241 241 242 244 244 246 252 257 257 261 266 266 268 272 275
276 7 OBJECT DESCRIPTION 281 7.1 OVERVIEW 281 7.2 BOUNDARY DESCRIPTIONS
282 7.2.1 BOUNDARY AND REGION 282 7.2.2 CHAIN CODES 283 7.2.3 FOURIER
DESCRIPTORS 285 7.2.3.1 BASIS OF FOURIER DESCRIPTORS 286 7.2.3.2 FOURIER
EXPANSION 287 7.2.3.3 SHIFT INVARIANCE 289 7.2.3.4 DISCRETE COMPUTATION
290 7.2.3.5 CUMULATIVE ANGULAR FUNETION 292 7.2.3.6 ELLIPTIC FOURIER
DESCRIPTORS 301 7.2.3.7 INVARIANCE 305 7.3 REGION DESCRIPTORS 311 7.3.1
BASIC REGION DESCRIPTORS 311 7.3.2 MOMENTS 315 7.3.2.1 BASIC PROPERTIES
315 7.3.2.2 INVARIANT MOMENTS 318 7.3.2.3 ZERNIKE MOMENTS 320 7.3.2.4
OTHER MOMENTS 324 7.4 FURTHER READING 325 7.5 REFERENCES 326 VIII
CONTENTS 8 INTRODUCTION TO TEXTURE DESCRIPTION, SEGMENTATION AND
CLASSIFICATION 329 8.1 OVERVIEW 329 8.2 WHAT IS TEXTURE? 330 8.3 TEXTURE
DESCRIPTION 332 8.3.1 PERFORMANCE REQUIREMENTS 332 8.3.2 STRUCTURAL
APPROACHES 332 8.3.3 STATISTICAL APPROACHES 335 8.3.4 COMBINATION
APPROACHES 337 8.4 CLASSIFICATION 339 8.4.1 THE AE-NEAREST NEIGHBOUR RULE
339 8.4.2 OTHER CLASSIFICATION APPROACHES 343 8.5 SEGMENTATION 343 8.6
FURTHER READING 345 8.7 REFERENCES 346 9 APPENDIX 1: EXAMPLE WORKSHEETS
349 9.1 EXAMPLE MATHCAD WORKSHEET FOR CHAPTER 3 349 9.2 EXAMPLE MATLAB
WORKSHEET FOR CHAPTER 4 352 10 APPENDIX 2: CAMERA GEOMETRY FUNDAMENTAL
355 10.1 IMAGE GEOMETRY 355 10.2 PERSPECTIVE CAMERA 355 10.3 PERSPECTIVE
CAMERA MODEL 357 10.3.1 HOMOGENEOUS COORDINATES AND PROJECTIVE GEOMETRY
357 10.3.1.1 REPRESENTATION OF A LINE AND DUALITY 358 10.3.1.2 IDEAL
POINTS 358 10.3.1.3 TRANSFORMATIONS IN THE PROJECTIVE SPACE 359 10.3.2
PERSPECTIVE CAMERA MODEL ANALYSIS 360 10.3.3 PARAMETERS OF THE
PERSPECTIVE CAMERA MODEL 363 10.4 AFFINE CAMERA 364 10.4.1 AFFINE CAMERA
MODEL 365 10.4.2 AFFINE CAMERA MODEL AND THE PERSPECTIVE PROJECTION 366
10.4.3 PARAMETERS OF THE AFFINE CAMERA MODEL 368 10.5 WEAK PERSPECTIVE
MODEL 369 10.6 EXAMPLE OF CAMERA MODEIS 371 10.7 DISCUSSION 379 10.8
REFERENCES 380 11 APPENDIX 3: LEAST SQUARES ANALYSIS 381 11.1 THE LEAST
SQUARES CRITERION 381 11.2 CURVE FITTING BY LEAST SQUARES 382 CONTENTS
IX 12 APPENDIX 4: PRINCIPAL COMPONENTS ANALYSIS 385 12.1 12.2 12.3 12.4
12.5 12.6 12.7 12.8 12.9 12.10 12.11 INTRODUCTION DATA COVARIANCE
COVARIANCE MATRIX DATA TRANSFORMATION INVERSE TRANSFORMATION
EIGENPROBLEM SOLVING THE EIGENPROBLEM PCA METHOD SUMMARY EXAMPLE
REFERENCES 385 385 386 388 389 390 391 392 392 393 398 INDEX 399 X
CONTENTS
|
| adam_txt |
FEATURE EXTRACTION AND IMAGE PROCESSING SECOND EDITION MARK S. NIXON
ALBERTO S. AGUADO AMSTERDAM * BOSTON * HEIDELBERG * LONDON * NEW YORK *
OXFORD PARIS * SAN DIEGO * SAN FRANCISCO * SINGAPORE * SYDNEY * TOKYO
ACADEMIC PRESS IS AN IMPRINT OF ELSEVIER CONTENTS PREFACE XI 1 1 3 4 6 7
9 10 12 14 15 16 16 21 24 24 25 28 29 29 2 IMAGES, SAMPLING AND
FREQUENCY DOMAIN PROCESSING 33 2.1 OVERVIEW 33 2.2 IMAGE FORMATION 34
2.3 THE FOURIER TRANSFORM 37 2.4 THE SAMPLING CRITERION 43 2.5 THE
DISCRETE FOURIER TRANSFORM 47 2.5.1 ONE-DIMENSIONAL TRANSFORM 47 2.5.2
TWO-DIMENSIONAL TRANSFORM 49 2.6 OTHER PROPERTIES OF THE FOURIER
TRANSFORM 54 2.6.1 SHIFT INVARIANCE 54 2.6.2 ROTATION 56 2.6.3 FREQUENCY
SCALING 56 2.6.4 SUPERPOSITION (LINEARITY) 57 INTRODUCTION 1.1 1.2 1.3
1.4 1.5 1.6 1.7 1.8 OVERVIEW HUMAN AND COMPUTER VISION THE HUMAN VISION
SYSTEM 1.3.1 THE EYE 1.3.2 THE NEURAL SYSTEM 1.3.3 PROCESSING COMPUTER
VISION SYSTEMS 1.4.1 CAMERAS 1.4.2 COMPUTER INTERFACES 1.4.3 PROCESSING
AN IMAGE MATHEMATICAL SYSTEMS 1.5.1 MATHEMATICAL TOOLS 1.5.2 HELLO
MATHCAD, HELLO IMAGES! 1.5.3 HELLO MATLAB! ASSOCIATED LITERATURE 1.6.1
JOURNALS AND MAGAZINES 1.6.2 TEXTBOOKS 1.6.3 THE WEB CONCLUSIONS
REFERENCES V 2.7 TRANSFORMS OTHER THAN FOURIER 58 2.7.1 DISCRETE COSINE
TRANSFORM 58 2.7.2 DISCRETE HARTLEY TRANSFORM 59 2.7.3 INTRODUCTORY
WAVELETS: THE GABOR WAVELET 61 2.7.4 OTHER TRANSFORMS 63 2.8
APPLICATIONS USING FREQUENCY DOMAIN PROPERTIES 64 2.9 FURTHER READING 65
2.10 REFERENCES 66 BASIC IMAGE PROCESSING OPERATIONS 69 3.1 OVERVIEW 69
3.2 HISTOGRAMS 70 3.3 POINT OPERATORS 71 3.3.1 BASIC POINT OPERATIONS 71
3.3.2 HISTOGRAM NORMALIZATION 74 3.3.3 HISTOGRAM EQUALIZATION 75 3.3.4
THRESHOLDING 77 3.4 GROUP OPERATIONS 81 3.4.1 TEMPLATE CONVOLUTION 81
3.4.2 AVERAGING OPERATOR 84 3.4.3 ON DIFFERENT TEMPLATE SIZE 87 3.4.4
GAUSSIAN AVERAGING OPERATOR 88 3.5 OTHER STATISTICAL OPERATORS 90 3.5.1
MORE ON AVERAGING 90 3.5.2 MEDIAN FUETER 91 3.5.3 MODE FILTER 94 3.5.4
ANISOTROPIE DIFFUSION 96 3.5.5 FORCE FIELD TRANSFORM 101 3.5.6
COMPARISON OF STATISTICAL OPERATORS 102 3.6 MATHEMATICAL MORPHOLOGY 103
3.6.1 MORPHOLOGICAL OPERATORS 104 3.6.2 GREY-LEVEL MORPHOLOGY 107 3.6.3
GREY-LEVEL EROSION AND DILATION 108 3.6.4 MINKOWSKI OPERATORS 109 3.7
FURTHER READING 112 3.8 REFERENCES 113 LOW-LEVEL FEATURE EXTRACTION
(INCLUDING EDGE DETECTION) 115 4.1 OVERVIEW 115 4.2 FIRST ORDER EDGE
DETECTION OPERATORS 117 4.2.1 BASIC OPERATORS 117 4.2.2 ANALYSIS OF THE
BASIC OPERATORS 119 4.2.3 PREWITT EDGE DETECTION OPERATOR 121 4.2.4
SOBEL EDGE DETECTION OPERATOR 123 4.2.5 CANNY EDGE DETECTION OPERATOR
129 CONTENTS 4.3 SECOND ORDER EDGE DETECTION OPERATORS 137 4.3.1
MOTIVATION 137 4.3.2 BASIC OPERATORS: THE LAPLACIAN 137 4.3.3
MARR-HILDRETH OPERATOR 139 4.4 OTHER EDGE DETECTION OPERATORS 144 4.5
COMPARISON OF EDGE DETECTION OPERATORS 145 4.6 FURTHER READING ON EDGE
DETECTION 146 4.7 PHASE CONGRUENCY 147 4.8 LOCALIZED FEATURE EXTRACTION
152 4.8.1 DETECTING IMAGE CURVATURE (CORNER EXTRACTION) 153 4.8.1.1
DEFINITION OF CURVATURE 153 4.8.1.2 COMPUTING DIFFERENCES IN EDGE
DIRECTION 154 4.8.1.3 MEASURING CURVATURE BY CHANGES IN INTENSITY
(DIFFERENTIATION) 156 4.8.1.4 MORAVEC AND HARRIS DETECTORS 159 4.8.1.5
FURTHER READING ON CURVATURE 163 4.8.2 MODERN APPROACHES: REGION/PATCH
ANALYSIS 163 4.8.2.1 SCALE INVARIANT FEATURE TRANSFORM 163 4.8.2.2
SALIENCY 166 4.8.2.3 OTHER TECHNIQUES AND PERFORMANCE ISSUES 167 4.9
DESCRIBING IMAGE MOTION 167 4.9.1 AREA-BASED APPROACH 168 4.9.2
DIFFERENTIAL APPROACH 171 4.9.3 FURTHER READING ON OPTICAL FLOW 177 4.10
CONCLUSIONS 178 4.11 REFERENCES 178 5 FEATURE EXTRACTION BY SHAPE
MATCHIN G 183 5.1 OVERVIEW 183 5.2 THRESHOLDING AND SUBTRACTION 184 5.3
TEMPLATE MATCHING 186 5.3.1 DEFINITION 186 5.3.2 FOURIER TRANSFORM
IMPLEMENTATION 193 5.3.3 DISCUSSION OF TEMPLATE MATCHING 196 5.4 HOUGH
TRANSFORM 196 5.4.1 OVERVIEW 196 5.4.2 LINES 197 5.4.3 HOUGH TRANSFORM
FOR CIRCLES 203 5.4.4 HOUGH TRANSFORM FOR ELLIPSES 207 5.4.5 PARAMETER
SPACE DECOMPOSITION 210 5.4.5.1 PARAMETER SPACE REDUCTION FOR LINES 210
5.4.5.2 PARAMETER SPACE REDUCTION FOR CIRCLES 212 5.4.5.3 PARAMETER
SPACE REDUCTION FOR ELLIPSES 217 5.5 GENERALIZED HOUGH TRANSFORM 221
5.5.1 FORMAL DEFINITION OF THE GHT 221 5.5.2 POLAR DEFINITION 223
CONTENTS VII 6.1 6.2 6.3 6.4 6.5 6.6 6.7 OVERVIEW DEFORMABLE TEMPLATES
ACTIVE CONTOURS (SNAKES) 6.3.1 BASICS 6.3.2 THE GREEDY ALGORITHM FOR
SNAKES 6.3.3 COMPLETE (KASS) SNAKE IMPLEMENTATION 6.3.4 OTHER SNAKE
APPROACHES 6.3.5 FURTHER SNAKE DEVELOPMENTS 6.3.6 GEOMETRIE ACTIVE
CONTOURS SHAPE SKELETONIZATION 6.4.1 DISTANCE TRANSFORMS 6.4.2 SYMMETRY
FLEXIBLE SHAPE MODEIS: ACTIVE SHAPE AND ACTIVE APPEARANCE FURTHER
READING REFERENCES 5.5.3 THE GHT TECHNIQUE 224 5.5.4 INVARIANT GHT 228
5.6 OTHER EXTENSIONS TO THE HOUGH TRANSFORM 235 5.7 FURTHER READING 236
5.8 REFERENCES 237 6 FLEXIBLE SHAPE EXTRACTION (SNAKES AND OTHER
TECHNIQUES) 241 241 242 244 244 246 252 257 257 261 266 266 268 272 275
276 7 OBJECT DESCRIPTION 281 7.1 OVERVIEW 281 7.2 BOUNDARY DESCRIPTIONS
282 7.2.1 BOUNDARY AND REGION 282 7.2.2 CHAIN CODES 283 7.2.3 FOURIER
DESCRIPTORS 285 7.2.3.1 BASIS OF FOURIER DESCRIPTORS 286 7.2.3.2 FOURIER
EXPANSION 287 7.2.3.3 SHIFT INVARIANCE 289 7.2.3.4 DISCRETE COMPUTATION
290 7.2.3.5 CUMULATIVE ANGULAR FUNETION 292 7.2.3.6 ELLIPTIC FOURIER
DESCRIPTORS 301 7.2.3.7 INVARIANCE 305 7.3 REGION DESCRIPTORS 311 7.3.1
BASIC REGION DESCRIPTORS 311 7.3.2 MOMENTS 315 7.3.2.1 BASIC PROPERTIES
315 7.3.2.2 INVARIANT MOMENTS 318 7.3.2.3 ZERNIKE MOMENTS 320 7.3.2.4
OTHER MOMENTS 324 7.4 FURTHER READING 325 7.5 REFERENCES 326 VIII
CONTENTS 8 INTRODUCTION TO TEXTURE DESCRIPTION, SEGMENTATION AND
CLASSIFICATION 329 8.1 OVERVIEW 329 8.2 WHAT IS TEXTURE? 330 8.3 TEXTURE
DESCRIPTION 332 8.3.1 PERFORMANCE REQUIREMENTS 332 8.3.2 STRUCTURAL
APPROACHES 332 8.3.3 STATISTICAL APPROACHES 335 8.3.4 COMBINATION
APPROACHES 337 8.4 CLASSIFICATION 339 8.4.1 THE AE-NEAREST NEIGHBOUR RULE
339 8.4.2 OTHER CLASSIFICATION APPROACHES 343 8.5 SEGMENTATION 343 8.6
FURTHER READING 345 8.7 REFERENCES 346 9 APPENDIX 1: EXAMPLE WORKSHEETS
349 9.1 EXAMPLE MATHCAD WORKSHEET FOR CHAPTER 3 349 9.2 EXAMPLE MATLAB
WORKSHEET FOR CHAPTER 4 352 10 APPENDIX 2: CAMERA GEOMETRY FUNDAMENTAL
355 10.1 IMAGE GEOMETRY 355 10.2 PERSPECTIVE CAMERA 355 10.3 PERSPECTIVE
CAMERA MODEL 357 10.3.1 HOMOGENEOUS COORDINATES AND PROJECTIVE GEOMETRY
357 10.3.1.1 REPRESENTATION OF A LINE AND DUALITY 358 10.3.1.2 IDEAL
POINTS 358 10.3.1.3 TRANSFORMATIONS IN THE PROJECTIVE SPACE 359 10.3.2
PERSPECTIVE CAMERA MODEL ANALYSIS 360 10.3.3 PARAMETERS OF THE
PERSPECTIVE CAMERA MODEL 363 10.4 AFFINE CAMERA 364 10.4.1 AFFINE CAMERA
MODEL 365 10.4.2 AFFINE CAMERA MODEL AND THE PERSPECTIVE PROJECTION 366
10.4.3 PARAMETERS OF THE AFFINE CAMERA MODEL 368 10.5 WEAK PERSPECTIVE
MODEL 369 10.6 EXAMPLE OF CAMERA MODEIS 371 10.7 DISCUSSION 379 10.8
REFERENCES 380 11 APPENDIX 3: LEAST SQUARES ANALYSIS 381 11.1 THE LEAST
SQUARES CRITERION 381 11.2 CURVE FITTING BY LEAST SQUARES 382 CONTENTS
IX 12 APPENDIX 4: PRINCIPAL COMPONENTS ANALYSIS 385 12.1 12.2 12.3 12.4
12.5 12.6 12.7 12.8 12.9 12.10 12.11 INTRODUCTION DATA COVARIANCE
COVARIANCE MATRIX DATA TRANSFORMATION INVERSE TRANSFORMATION
EIGENPROBLEM SOLVING THE EIGENPROBLEM PCA METHOD SUMMARY EXAMPLE
REFERENCES 385 385 386 388 389 390 391 392 392 393 398 INDEX 399 X
CONTENTS |
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| author | Nixon, Mark S. Aguado, Alberto S. |
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| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV035114687 |
| illustrated | Illustrated |
| index_date | 2024-07-02T22:19:04Z |
| indexdate | 2024-07-09T21:22:38Z |
| institution | BVB |
| isbn | 0123725380 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016782454 |
| oclc_num | 232364927 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM DE-522 DE-898 DE-BY-UBR |
| owner_facet | DE-91 DE-BY-TUM DE-522 DE-898 DE-BY-UBR |
| physical | XV, 406 S. Ill., graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Elsevier, Acad. Press |
| record_format | marc |
| spelling | Nixon, Mark S. Verfasser aut Feature extraction and image processing Mark S. Nixon ; Alberto S. Aguado 2. ed. Amsterdam [u.a.] Elsevier, Acad. Press 2008 XV, 406 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Literaturangaben Mathematik Computer vision Computer vision Mathematics Pattern recognition systems Image processing Digital techniques Bildverarbeitung (DE-588)4006684-8 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 s DE-604 Bildverarbeitung (DE-588)4006684-8 s 1\p DE-604 Aguado, Alberto S. Verfasser aut GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016782454&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 |
| spellingShingle | Nixon, Mark S. Aguado, Alberto S. Feature extraction and image processing Mathematik Computer vision Computer vision Mathematics Pattern recognition systems Image processing Digital techniques Bildverarbeitung (DE-588)4006684-8 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
| subject_GND | (DE-588)4006684-8 (DE-588)4129594-8 |
| title | Feature extraction and image processing |
| title_auth | Feature extraction and image processing |
| title_exact_search | Feature extraction and image processing |
| title_exact_search_txtP | Feature extraction and image processing |
| title_full | Feature extraction and image processing Mark S. Nixon ; Alberto S. Aguado |
| title_fullStr | Feature extraction and image processing Mark S. Nixon ; Alberto S. Aguado |
| title_full_unstemmed | Feature extraction and image processing Mark S. Nixon ; Alberto S. Aguado |
| title_short | Feature extraction and image processing |
| title_sort | feature extraction and image processing |
| topic | Mathematik Computer vision Computer vision Mathematics Pattern recognition systems Image processing Digital techniques Bildverarbeitung (DE-588)4006684-8 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
| topic_facet | Mathematik Computer vision Computer vision Mathematics Pattern recognition systems Image processing Digital techniques Bildverarbeitung Maschinelles Sehen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016782454&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT nixonmarks featureextractionandimageprocessing AT aguadoalbertos featureextractionandimageprocessing |