Emerging topics in computer vision:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Upper Saddle River, NJ
Prentice Hall
2005
|
| Ausgabe: | 1. print. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XIX, 661 S. Ill., graph. Darst. 2 DVDs (12 cm) |
| ISBN: | 0131013661 |
Internformat
MARC
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| 245 | 1 | 0 | |a Emerging topics in computer vision |c Gérard Medioni ... |
| 250 | |a 1. print. | ||
| 264 | 1 | |a Upper Saddle River, NJ |b Prentice Hall |c 2005 | |
| 300 | |a XIX, 661 S. |b Ill., graph. Darst. |e 2 DVDs (12 cm) | ||
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Datensatz im Suchindex
| _version_ | 1804134688339525632 |
|---|---|
| adam_text | Contents
PREFACE xv
CONTRIBUTORS xvi
1 INTRODUCTION 1
PART I
FUNDAMENTALS IN COMPUTER VISION 2
2 CAMERA CALIBRATION 4
Zhengyou Zhang
2.1 Introduction 4
2.2 Notation and Problem Statement 6
2.2.1 Pinhole camera model 7
2.2.2 Absolute conic 8
2.3 Camera Calibration with 3D Objects 10
2.3.1 Feature extraction 11
2.3.2 Linear estimation of the camera projection matrix 12
2.3.3 Recover intrinsic and extrinsic parameters from P 13
2.3.4 Refine calibration parameters
through a nonlinear optimization 14
2.3.5 Lens distortion 15
2.3.6 An example 16
2.4 Camera Calibration with 2D Objects: Plane Based Technique 18
2.4.1 Homography between the model plane and its image 18
2.4.2 Constraints on the intrinsic parameters 18
2.4.3 Geometric interpretation 19
2.4.4 Closed form solution 20
2.4.5 Maximum likelihood estimation 21
2.4.6 Dealing with radial distortion 22
viii Contents
2.4.7 Summary 23
2.4.8 Experimental results 24
2.4.9 Related work 26
2.5 Solving Camera Calibration with ID Objects 27
2.5.1 Setups with free moving ID calibration objects 27
2.5.2 Setups with ID calibration objects moving
around a fixed point 28
2.5.3 Basic equations 30
2.5.4 Closed form solution 31
2.5.5 Nonlinear optimization 32
2.5.6 Estimating the fixed point 33
2.5.7 Experimental results 35
2.6 Self Calibration 39
2.7 Conclusion 39
2.8 Appendix: Estimating Homography Between Plane and Image 40
Bibliography 40
3 MULTIPLE VIEW GEOMETRY 44
Anders Heyden and Marc Pollefeys
3.1 Introduction 44
3.2 Projective Geometry 45
3.2.1 The central perspective transformation 45
3.2.2 Projective spaces 47
3.2.3 Homogeneous coordinates 48
3.2.4 Duality 51
3.2.5 Projective transformations 53
3.3 Tensor Calculus 55
3.4 Modeling Cameras 57
3.4.1 The pinhole camera 57
3.4.2 The camera matrix 58
3.4.3 The intrinsic parameters 59
3.4.4 The extrinsic parameters 59
3.4.5 Properties of the pinhole camera 62
3.5 Multiple View Geometry 62
3.5.1 The structure and motion problem 63
3.5.2 The two view case 63
3.5.3 Multiview constraints and tensors 69
3.6 Structure and Motion I 74
3.6.1 Resection 75
Contents ix
3.6.2 Intersection 75
3.6.3 Linear estimation of tensors 75
3.6.4 Factorization 78
3.7 Structure and Motion II 80
3.7.1 Two view geometry computation 80
3.7.2 Structure and motion recovery 82
3.8 Autocalibration 87
3.9 Dense Depth Estimation 90
3.9.1 Rectification 90
3.9.2 Stereo matching 91
3.9.3 Multiview linking 93
3.10 Visual Modeling 95
3.10.1 3D surface reconstruction 95
3.10.2 Image based rendering 98
3.10.3 Match moving 99
3.11 Conclusion 100
Bibliography 102
4 ROBUST TECHNIQUES FOR COMPUTER VISION 107
Peter Meer
4.1 Robustness in Visual Tasks 107
4.2 Models and Estimation Problems 111
4.2.1 Elements of a model 111
4.2.2 Estimation of a model 117
4.2.3 Robustness of an estimator 120
4.2.4 Definition of robustness 123
4.2.5 Taxonomy of estimation problems 126
4.2.6 Linear EIV regression model 129
4.2.7 Objective function optimization 133
4.3 Location Estimation 139
4.3.1 Why nonparametric methods? 139
4.3.2 Kernel density estimation 141
4.3.3 Adaptive mean shift 146
4.3.4 Applications 150
4.4 Robust Regression 157
4.4.1 Least squares family 158
4.4.2 M estimators 163
4.4.3 Median absolute deviation scale estimate 166
4.4.4 LMedS, RANSAC, and Hough transform 169
X Contents
4.4.5 The pbM estimator 174
4.4.6 Applications 178
4.4.7 Structured outliers 181
4.5 Conclusion 183
Bibliography 184
5 THE TENSOR VOTING FRAMEWORK 191
Gerard Medioni and Philippos Mordohai
5.1 Introduction 191
5.1.1 Motivation 192
5.1.2 Desirable descriptions 194
5.1.3 Our approach 195
5.1.4 Chapter overview 198
5.2 Related Work 198
5.3 Tensor Voting in 2D 203
5.3.1 Second order representation and voting in 2D 204
5.3.2 First order representation and voting in 2D 209
5.3.3 Voting fields 213
5.3.4 Vote analysis 216
5.3.5 Results in 2D 219
5.3.6 Illusory contours 220
5.4 Tensor Voting in 3D 222
5.4.1 Representation in 3D 223
5.4.2 Voting in 3D 225
5.4.3 Vote analysis 227
5.4.4 Results in 3D 229
5.5 Tensor Voting in iVD 231
5.5.1 Computational complexity 233
5.6 Application to Computer Vision Problems 234
5.6.1 Initial matching 235
5.6.2 Uniqueness 237
5.6.3 Discrete densification 237
5.6.4 Discontinuity localization 238
5.6.5 Stereo 241
5.6.6 Multiple view stereo 243
5.6.7 Visual motion from motion cues 244
5.6.8 Visual motion on real images 246
5.7 Conclusion and Future Work 247
Contents xi
5.8 Acknowledgments 251
Bibliography 252
PART II
APPLICATIONS IN COMPUTER VISION 256
6 IMAGE BASED LIGHTING 257
Paul E. Debevec
6.1 Basic Image Based Lighting 259
6.1.1 Capturing light 259
6.1.2 Illuminating synthetic objects with real light 262
6.1.3 Lighting entire environments with IBL 271
6.2 Advanced Image Based Lighting 271
6.2.1 Capturing a light probe in direct sunlight 274
6.2.2 Compositing objects into the scene including shadows 284
6.2.3 Image based lighting in Fiat Lux 289
6.2.4 Capturing and rendering spatially varying illumination 293
6.3 Image Based Relighting 297
6.4 Conclusion 301
Bibliography 303
7 COMPUTER VISION IN VISUAL EFFECTS 306
Doug Roble
7.1 Introduction 306
7.2 Computer Vision Problems Unique to Film 307
7.2.1 Welcome to the set 307
7.3 Feature Tracking 320
7.4 Optical Flow 322
7.5 Camera Tracking and Structure from Motion 326
7.6 The Future 331
Bibliography 332
8 CONTENT BASED IMAGE RETRIEVAL:
AN OVERVIEW 333
Theo Gevers and Arnold W. M. Smeulders
8.1 Overview of Chapter 334
8.2 Image Domains 339
8.2.1 Search modes 339
8.2.2 The sensory gap 341
8.2.3 The semantic gap 342
xii Contents
8.2.4 Discussion 343
8.3 Image Features 344
8.3.1 Color 345
8.3.2 Shape 349
8.3.3 Texture 350
8.3.4 Discussion 352
8.4 Representation and Indexing 353
8.4.1 Grouping data 353
8.4.2 Features accumulation 354
8.4.3 Feature accumulation and image partitioning 357
8.4.4 Salient features 359
8.4.5 Shape and object features 360
8.4.6 Structure and layout 361
8.4.7 Discussion 362
8.5 Similarity and Search 363
8.5.1 Semantic interpretation 363
8.5.2 Similarity between features 364
8.5.3 Similarity of object outlines 367
8.5.4 Similarity of object arrangements 368
8.5.5 Similarity of salient features 369
8.5.6 Discussion 369
8.6 Interaction and Learning 370
8.6.1 Interaction on a semantic level 370
8.6.2 Classification on a semantic level 371
8.6.3 Learning 371
8.6.4 Discussion 372
8.7 Conclusion 373
Bibliography 373
9 FACE DETECTION, ALIGNMENT, AND
RECOGNITION 385
Stan Z. Li and Juwei Lu
9.1 Introduction 385
9.2 Face Detection 388
9.2.1 Appearance based and learning based approaches 389
9.2.2 Preprocessing 391
9.2.3 Neural and kernel methods 394
9.2.4 Boosting based methods 394
9.2.5 Postprocessing 401
Contents xiii
9.2.6 Evaluation 401
9.3 Face Alignment 404
9.3.1 Active shape model 406
9.3.2 Active appearance model 407
9.3.3 Modeling shape from texture 408
9.3.4 Dealing with head pose 415
9.3.5 Evaluation 417
9.4 Face Recognition 419
9.4.1 Preprocessing 420
9.4.2 Feature extraction 420
9.4.3 Pattern classification 432
9.4.4 Evaluation 440
Bibliography 447
10 PERCEPTUAL INTERFACES 456
Matthew Turk and Mathias Kolsch
10.1 Introduction 456
10.2 Perceptual Interfaces and HCI 458
10.3 Multimodal Interfaces 465
10.4 Vision Based Interfaces 473
10.4.1 Terminology 478
10.4.2 Elements of VBI 480
10.4.3 Computer vision methods for VBI 492
10.4.4 VBI summary 505
10.5 Brain Computer Interfaces 506
10.6 Summary 509
Bibliography 511
PART III
PROGRAMMING FOR COMPUTER VISION 521
11 OPEN SOURCE COMPUTER VISION LIBRARY 522
Gary Bradski
11.1 Overview 522
11.1.1 Installation 523
11.1.2 Organization 528
11.1.3 Optimizations 530
11.2 Functional Groups: What s Good for What 533
11.2.1 By area 535
11.2.2 By task 539
xiv Contents
11.2.3 Demos and samples 544
11.3 Pictorial Tour 546
11.3.1 Functional groups 546
11.3.2 Demo tour 561
11.4 Programming Examples Using C/C++ 562
11.4.1 Read images from disk 567
11.4.2 Read AVIs from disk, or video from a camera 569
11.5 Other Interfaces 571
11.5.1 Ch 571
11.5.2 MATLAB 576
11.5.3 Lush 578
11.6 Appendix A 579
11.7 Appendix B 580
Bibliography 582
12 SOFTWARE ARCHITECTURE FOR
COMPUTER VISION 585
Alexandre R. J. Frangois
12.1 Introduction 585
12.1.1 Motivation 585
12.1.2 Contribution 589
12.1.3 Outline 589
12.2 SAL A Software Architecture Model 590
12.2.1 Beyond pipes and niters 590
12.2.2 The SAI architectural style 597
12.2.3 Example designs 601
12.2.4 Architectural properties 620
12.3 MFSM: An Architectural Middleware 623
12.3.1 MFSM overview 624
12.3.2 A First image manipulation example 627
12.3.3 Custom elements 635
12.3.4 A shared memory access example 644
12.4 Conclusion 650
12.4.1 Summary 650
12.4.2 Perspectives 651
12.5 Acknowledgments 652
Bibliography 653
INDEX 655
|
| adam_txt |
Contents
PREFACE xv
CONTRIBUTORS xvi
1 INTRODUCTION 1
PART I
FUNDAMENTALS IN COMPUTER VISION 2
2 CAMERA CALIBRATION 4
Zhengyou Zhang
2.1 Introduction 4
2.2 Notation and Problem Statement 6
2.2.1 Pinhole camera model 7
2.2.2 Absolute conic 8
2.3 Camera Calibration with 3D Objects 10
2.3.1 Feature extraction 11
2.3.2 Linear estimation of the camera projection matrix 12
2.3.3 Recover intrinsic and extrinsic parameters from P 13
2.3.4 Refine calibration parameters
through a nonlinear optimization 14
2.3.5 Lens distortion 15
2.3.6 An example 16
2.4 Camera Calibration with 2D Objects: Plane Based Technique 18
2.4.1 Homography between the model plane and its image 18
2.4.2 Constraints on the intrinsic parameters 18
2.4.3 Geometric interpretation 19
2.4.4 Closed form solution 20
2.4.5 Maximum likelihood estimation 21
2.4.6 Dealing with radial distortion 22
viii Contents
2.4.7 Summary 23
2.4.8 Experimental results 24
2.4.9 Related work 26
2.5 Solving Camera Calibration with ID Objects 27
2.5.1 Setups with free moving ID calibration objects 27
2.5.2 Setups with ID calibration objects moving
around a fixed point 28
2.5.3 Basic equations 30
2.5.4 Closed form solution 31
2.5.5 Nonlinear optimization 32
2.5.6 Estimating the fixed point 33
2.5.7 Experimental results 35
2.6 Self Calibration 39
2.7 Conclusion 39
2.8 Appendix: Estimating Homography Between Plane and Image 40
Bibliography 40
3 MULTIPLE VIEW GEOMETRY 44
Anders Heyden and Marc Pollefeys
3.1 Introduction 44
3.2 Projective Geometry 45
3.2.1 The central perspective transformation 45
3.2.2 Projective spaces 47
3.2.3 Homogeneous coordinates 48
3.2.4 Duality 51
3.2.5 Projective transformations 53
3.3 Tensor Calculus 55
3.4 Modeling Cameras 57
3.4.1 The pinhole camera 57
3.4.2 The camera matrix 58
3.4.3 The intrinsic parameters 59
3.4.4 The extrinsic parameters 59
3.4.5 Properties of the pinhole camera 62
3.5 Multiple View Geometry 62
3.5.1 The structure and motion problem 63
3.5.2 The two view case 63
3.5.3 Multiview constraints and tensors 69
3.6 Structure and Motion I 74
3.6.1 Resection 75
Contents ix
3.6.2 Intersection 75
3.6.3 Linear estimation of tensors 75
3.6.4 Factorization 78
3.7 Structure and Motion II 80
3.7.1 Two view geometry computation 80
3.7.2 Structure and motion recovery 82
3.8 Autocalibration 87
3.9 Dense Depth Estimation 90
3.9.1 Rectification 90
3.9.2 Stereo matching 91
3.9.3 Multiview linking 93
3.10 Visual Modeling 95
3.10.1 3D surface reconstruction 95
3.10.2 Image based rendering 98
3.10.3 Match moving 99
3.11 Conclusion 100
Bibliography 102
4 ROBUST TECHNIQUES FOR COMPUTER VISION 107
Peter Meer
4.1 Robustness in Visual Tasks 107
4.2 Models and Estimation Problems 111
4.2.1 Elements of a model 111
4.2.2 Estimation of a model 117
4.2.3 Robustness of an estimator 120
4.2.4 Definition of robustness 123
4.2.5 Taxonomy of estimation problems 126
4.2.6 Linear EIV regression model 129
4.2.7 Objective function optimization 133
4.3 Location Estimation 139
4.3.1 Why nonparametric methods? 139
4.3.2 Kernel density estimation 141
4.3.3 Adaptive mean shift 146
4.3.4 Applications 150
4.4 Robust Regression 157
4.4.1 Least squares family 158
4.4.2 M estimators 163
4.4.3 Median absolute deviation scale estimate 166
4.4.4 LMedS, RANSAC, and Hough transform 169
X Contents
4.4.5 The pbM estimator 174
4.4.6 Applications 178
4.4.7 Structured outliers 181
4.5 Conclusion 183
Bibliography 184
5 THE TENSOR VOTING FRAMEWORK 191
Gerard Medioni and Philippos Mordohai
5.1 Introduction 191
5.1.1 Motivation 192
5.1.2 Desirable descriptions 194
5.1.3 Our approach 195
5.1.4 Chapter overview 198
5.2 Related Work 198
5.3 Tensor Voting in 2D 203
5.3.1 Second order representation and voting in 2D 204
5.3.2 First order representation and voting in 2D 209
5.3.3 Voting fields 213
5.3.4 Vote analysis 216
5.3.5 Results in 2D 219
5.3.6 Illusory contours 220
5.4 Tensor Voting in 3D 222
5.4.1 Representation in 3D 223
5.4.2 Voting in 3D 225
5.4.3 Vote analysis 227
5.4.4 Results in 3D 229
5.5 Tensor Voting in iVD 231
5.5.1 Computational complexity 233
5.6 Application to Computer Vision Problems 234
5.6.1 Initial matching 235
5.6.2 Uniqueness 237
5.6.3 Discrete densification 237
5.6.4 Discontinuity localization 238
5.6.5 Stereo 241
5.6.6 Multiple view stereo 243
5.6.7 Visual motion from motion cues 244
5.6.8 Visual motion on real images 246
5.7 Conclusion and Future Work 247
Contents xi
5.8 Acknowledgments 251
Bibliography 252
PART II
APPLICATIONS IN COMPUTER VISION 256
6 IMAGE BASED LIGHTING 257
Paul E. Debevec
6.1 Basic Image Based Lighting 259
6.1.1 Capturing light 259
6.1.2 Illuminating synthetic objects with real light 262
6.1.3 Lighting entire environments with IBL 271
6.2 Advanced Image Based Lighting 271
6.2.1 Capturing a light probe in direct sunlight 274
6.2.2 Compositing objects into the scene including shadows 284
6.2.3 Image based lighting in Fiat Lux 289
6.2.4 Capturing and rendering spatially varying illumination 293
6.3 Image Based Relighting 297
6.4 Conclusion 301
Bibliography 303
7 COMPUTER VISION IN VISUAL EFFECTS 306
Doug Roble
7.1 Introduction 306
7.2 Computer Vision Problems Unique to Film 307
7.2.1 Welcome to the set 307
7.3 Feature Tracking 320
7.4 Optical Flow 322
7.5 Camera Tracking and Structure from Motion 326
7.6 The Future 331
Bibliography 332
8 CONTENT BASED IMAGE RETRIEVAL:
AN OVERVIEW 333
Theo Gevers and Arnold W. M. Smeulders
8.1 Overview of Chapter 334
8.2 Image Domains 339
8.2.1 Search modes 339
8.2.2 The sensory gap 341
8.2.3 The semantic gap 342
xii Contents
8.2.4 Discussion 343
8.3 Image Features 344
8.3.1 Color 345
8.3.2 Shape 349
8.3.3 Texture 350
8.3.4 Discussion 352
8.4 Representation and Indexing 353
8.4.1 Grouping data 353
8.4.2 Features accumulation 354
8.4.3 Feature accumulation and image partitioning 357
8.4.4 Salient features 359
8.4.5 Shape and object features 360
8.4.6 Structure and layout 361
8.4.7 Discussion 362
8.5 Similarity and Search 363
8.5.1 Semantic interpretation 363
8.5.2 Similarity between features 364
8.5.3 Similarity of object outlines 367
8.5.4 Similarity of object arrangements 368
8.5.5 Similarity of salient features 369
8.5.6 Discussion 369
8.6 Interaction and Learning 370
8.6.1 Interaction on a semantic level 370
8.6.2 Classification on a semantic level 371
8.6.3 Learning 371
8.6.4 Discussion 372
8.7 Conclusion 373
Bibliography 373
9 FACE DETECTION, ALIGNMENT, AND
RECOGNITION 385
Stan Z. Li and Juwei Lu
9.1 Introduction 385
9.2 Face Detection 388
9.2.1 Appearance based and learning based approaches 389
9.2.2 Preprocessing 391
9.2.3 Neural and kernel methods 394
9.2.4 Boosting based methods 394
9.2.5 Postprocessing 401
Contents xiii
9.2.6 Evaluation 401
9.3 Face Alignment 404
9.3.1 Active shape model 406
9.3.2 Active appearance model 407
9.3.3 Modeling shape from texture 408
9.3.4 Dealing with head pose 415
9.3.5 Evaluation 417
9.4 Face Recognition 419
9.4.1 Preprocessing 420
9.4.2 Feature extraction 420
9.4.3 Pattern classification 432
9.4.4 Evaluation 440
Bibliography 447
10 PERCEPTUAL INTERFACES 456
Matthew Turk and Mathias Kolsch
10.1 Introduction 456
10.2 Perceptual Interfaces and HCI 458
10.3 Multimodal Interfaces 465
10.4 Vision Based Interfaces 473
10.4.1 Terminology 478
10.4.2 Elements of VBI 480
10.4.3 Computer vision methods for VBI 492
10.4.4 VBI summary 505
10.5 Brain Computer Interfaces 506
10.6 Summary 509
Bibliography 511
PART III
PROGRAMMING FOR COMPUTER VISION 521
11 OPEN SOURCE COMPUTER VISION LIBRARY 522
Gary Bradski
11.1 Overview 522
11.1.1 Installation 523
11.1.2 Organization 528
11.1.3 Optimizations 530
11.2 Functional Groups: What's Good for What 533
11.2.1 By area 535
11.2.2 By task 539
xiv Contents
11.2.3 Demos and samples 544
11.3 Pictorial Tour 546
11.3.1 Functional groups 546
11.3.2 Demo tour 561
11.4 Programming Examples Using C/C++ 562
11.4.1 Read images from disk 567
11.4.2 Read AVIs from disk, or video from a camera 569
11.5 Other Interfaces 571
11.5.1 Ch 571
11.5.2 MATLAB 576
11.5.3 Lush 578
11.6 Appendix A 579
11.7 Appendix B 580
Bibliography 582
12 SOFTWARE ARCHITECTURE FOR
COMPUTER VISION 585
Alexandre R. J. Frangois
12.1 Introduction 585
12.1.1 Motivation 585
12.1.2 Contribution 589
12.1.3 Outline 589
12.2 SAL A Software Architecture Model 590
12.2.1 Beyond pipes and niters 590
12.2.2 The SAI architectural style 597
12.2.3 Example designs 601
12.2.4 Architectural properties 620
12.3 MFSM: An Architectural Middleware 623
12.3.1 MFSM overview 624
12.3.2 A First image manipulation example 627
12.3.3 Custom elements 635
12.3.4 A shared memory access example 644
12.4 Conclusion 650
12.4.1 Summary 650
12.4.2 Perspectives 651
12.5 Acknowledgments 652
Bibliography 653
INDEX 655 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| building | Verbundindex |
| bvnumber | BV021240906 |
| callnumber-first | T - Technology |
| callnumber-label | TA1634 |
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| callnumber-search | TA1634 |
| callnumber-sort | TA 41634 |
| callnumber-subject | TA - General and Civil Engineering |
| classification_rvk | ST 330 |
| classification_tum | DAT 760f |
| ctrlnum | (OCoLC)54752819 (DE-599)BVBBV021240906 |
| dewey-full | 006.3/7 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/7 |
| dewey-search | 006.3/7 |
| dewey-sort | 16.3 17 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | 1. print. |
| format | Book |
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| id | DE-604.BV021240906 |
| illustrated | Illustrated |
| index_date | 2024-07-02T13:31:17Z |
| indexdate | 2024-07-09T20:28:35Z |
| institution | BVB |
| isbn | 0131013661 |
| language | English |
| lccn | 004044631 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014283605 |
| oclc_num | 54752819 |
| open_access_boolean | |
| owner | DE-20 DE-91 DE-BY-TUM |
| owner_facet | DE-20 DE-91 DE-BY-TUM |
| physical | XIX, 661 S. Ill., graph. Darst. 2 DVDs (12 cm) |
| publishDate | 2005 |
| publishDateSearch | 2005 |
| publishDateSort | 2005 |
| publisher | Prentice Hall |
| record_format | marc |
| spelling | Emerging topics in computer vision Gérard Medioni ... 1. print. Upper Saddle River, NJ Prentice Hall 2005 XIX, 661 S. Ill., graph. Darst. 2 DVDs (12 cm) txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Computer vision Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 s DE-604 Medioni, Gérard Sonstige oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014283605&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Emerging topics in computer vision Computer vision Maschinelles Sehen (DE-588)4129594-8 gnd |
| subject_GND | (DE-588)4129594-8 |
| title | Emerging topics in computer vision |
| title_auth | Emerging topics in computer vision |
| title_exact_search | Emerging topics in computer vision |
| title_exact_search_txtP | Emerging topics in computer vision |
| title_full | Emerging topics in computer vision Gérard Medioni ... |
| title_fullStr | Emerging topics in computer vision Gérard Medioni ... |
| title_full_unstemmed | Emerging topics in computer vision Gérard Medioni ... |
| title_short | Emerging topics in computer vision |
| title_sort | emerging topics in computer vision |
| topic | Computer vision Maschinelles Sehen (DE-588)4129594-8 gnd |
| topic_facet | Computer vision Maschinelles Sehen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014283605&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT medionigerard emergingtopicsincomputervision |