计算机与机器视觉理论、算法与实践 英文版 第4版PDF电子书下载

CHAPTER 1 Vision,the Challenge 1

1.1 Introduction―Man and His Senses 1

1.2 The Nature of Vision 2

1.2.1 The Process of Recognition 2

1.2.2 Tackling the Recognition Problem 4

1.2.3 Object Location 6

1.2.4 Scene Analysis 8

1.2.5 Vision as Inverse Graphics 9

1.3 From Automated Visual Inspection to Surveillance 10

1.4 What This Book is About 12

1.5 The Following Chapters 13

1.6 Bibliographical Notes 14

PART 1 LOW-LEVEL VISION 15

CHAPTER 2 Images and Imaging Operations 17

2.1 Introduction 18

2.1.1 Gray Scale Versus Color 19

2.2 Image Processing Operations 23

2.2.1 Some Basic Operations on Grayscale Images 24

2.2.2 Basic Operations on Binary Images 28

2.3 Convolutions and Point Spread Functions 32

2.4 Sequential Versus Parallel Operations 34

2.5 Concluding Remarks 36

2.6 Bibliographical and Historical Notes 36

2.7 Problems 36

CHAPTER 3 Basic Image Filtering Operations 38

3.1 Introduction 38

3.2 Noise Suppression by Gaussian Smoothing 40

3.3 Median Filters 43

3.4 Mode Filters 45

3.5 Rank Order Filters 52

3.6 Reducing Computational Load 54

3.7 Sharp-Unsharp Masking 55

3.8 Shifts Introduced by Median Filters 56

3.8.1 Continuum Model of Median Shifts 57

3.8.2 Generalization to Grayscale Images 59

3.8.3 Problems with Statistics 60

3.9 Discrete Model of Median Shifts 62

3.10 Shifts Introduced by Mode Filters 65

3.11 Shifts Introduced by Mean and Gaussian Filters 67

3.12 Shifts Introduced by Rank Order Filters 68

3.12.1 Shifts in Rectangular Neighborhoods 69

3.13 The Role of Filters in Industrial Applications of Vision 74

3.14 Color in Image Filtering 74

3.15 Concluding Remarks 76

3.16 Bibliographical and Historical Notes 77

3.16.1 More Recent Developments 78

3.17 Problems 79

CHAPTER 4 Thresholding Techniques 82

4.1 Introduction 83

4.2 Region-Growing Methods 83

4.3 Thresholding 84

4.3.1 Finding a Suitable Threshold 85

4.3.2 Tackling the Problem of Bias in Threshold Selection 86

4.3.3 Summary 88

4.4 Adaptive Thresholding 88

4.4.1 The Chow and Kaneko Approach 91

4.4.2 Local Thresholding Methods 92

4.5 More Thoroughgoing Approaches to Threshold Selection 93

4.5.1 Variance-Based Thresholding 95

4.5.2 Entropy-Based Thresholding 96

4.5.3 Maximum Likelihood Thresholding 97

4.6 The Global Valley Approach to Thresholding 98

4.7 Practical Results Obtained Using the Global ValleyMethod 101

4.8 Histogram Concavity Analysis 106

4.9 Concluding Remarks 107

4.10 Bibliographical and Historical Notes 108

4.10.1 More Recent Developments 109

4.11 Problems 110

CHAPTER 5 Edge Detection 111

5.1 Introduction 112

5.2 Basic Theory of Edge Detection 113

5.3 The Template Matching Approach 115

5.4 Theory of 3×3 Template Operators 116

5.5 The Design of Differential Gradient Operators 117

5.6 The Concept of a Circular Operator 118

5.7 Detailed Implementation of Circular Operators 120

5.8 The Systematic Design of Differential Edge Operators 122

5.9 Problems with the Above Approach―Some Alternative Schemes 123

5.10 Hysteresis Thresholding 126

5.11 The Canny Operator 128

5.12 The Laplacian Operator 131

5.13 Active Contours 134

5.14 Practical Results Obtained Using Active Contours 137

5.15 The Level Set Approach to Object Segmentation 140

5.16 The Graph Cut Approach to Object Segmentation 141

5.17 Concluding Remarks 145

5.18 Bibliographical and Historical Notes 146

5.18.1 More Recent Developments 147

5.19 Problems 148

CHAPTER 6 Corner and Interest Point Detection 149

6.1 Introduction 150

6.2 Template Matching 150

6.3 Second-Order Derivative Schemes 151

6.4 A Median Filter-Based Corner Detector 153

6.4.1 Analyzing the Operation of the Median Detector 154

6.4.2 Practical Results 156

6.5 The Harris Interest Point Operator 158

6.5.1 Corner Signals and Shifts for Various Geometric Configurations 161

6.5.2 Performance with Crossing Points and Junctions 162

6.5.3 Different Forms of the Harris Operator 165

6.6 Corner Orientation 166

6.7 Local Invariant Feature Detectors and Descriptors 168

6.7.1 Harris Scale and Affine-Invariant Detectors and Descriptors 171

6.7.2 Hessian Scale and Affine-Invariant Detectors and Descriptors 173

6.7.3 The SIFT Operator 173

6.7.4 The SURF Operator 174

6.7.5 Maximally Stable Extremal Regions 176

6.7.6 Comparison of the Various Invariant Feature Detectors 177

6.8 Concluding Remarks 180

6.9 Bibliographical and Historical Notes 181

6.9.1 More Recent Developments 184

6.10 Problems 184

CHAPTER 7 Mathematical Morphology 185

7.1 Introduction 185

7.2 Dilation and Erosion in Binary Images 186

7.2.1 Dilation and Erosion 186

7.2.2 Cancellation Effects 186

7.2.3 Modified Dilation and Erosion Operators 187

7.3 Mathematical Morphology 187

7.3.1 Generalized Morphological Dilation 187

7.3.2 Generalized Morphological Erosion 188

7.3.3 Duality Between Dilation and Erosion 189

7.3.4 Properties of Dilation and Erosion Operators 190

7.3.5 Closing and Opening 193

7.3.6 Summary of Basic Morphological Operations 195

7.4 Grayscale Processing 197

7.4.1 Morphological Edge Enhancement 198

7.4.2 Further Remarks on the Generalization to Grayscale Processing 199

7.5 Effect of Noise on Morphological Grouping Operations 201

7.5.1 Detailed Analysis 203

7.5.2 Discussion 205

7.6 Concluding Remarks 205

7.7 Bibliographical and Historical Notes 206

7.7.1 More Recent Developments 207

7.8 Problem 208

CHAPTER 8 Texture 209

8.1 Introduction 209

8.2 Some Basic Approaches to Texture Analysis 213

8.3 Graylevel Co-occurrence Matrices 213

8.4 Laws'Texture Energy Approach 217

8.5 Ade's Eigenfilter Approach 220

8.6 Appraisal of the Laws and Ade Approaches 221

8.7 Concluding Remarks 223

8.8 Bibliographical and Historical Notes 223

8.8.1 More Recent Developments 224

PART 2 INTERMEDIATE-LEVEL VISION 227

CHAPTER 9 Binary Shape Analysis 229

9.1 Introduction 230

9.2 Connectedness in Binary Images 230

9.3 Object Labeling and Counting 231

9.3.1 Solving the Labeling Problem in a More Complex Case 235

9.4 Size Filtering 238

9.5 Distance Functions and Their Uses 240

9.5.1 Local Maxima and Data Compression 243

9.6 Skeletons and Thinning 244

9.6.1 Crossing Number 247

9.6.2 Parallel and Sequential Implementations of Thinning 248

9.6.3 Guided Thinning 251

9.6.4 A Comment on the Nature of the Skeleton 251

9.6.5 Skeleton Node Analysis 251

9.6.6 Application of Skeletons for Shape Recognition 253

9.7 Other Measures for Shape Recognition 254

9.8 Boundary Tracking Procedures 257

9.9 Concluding Remarks 257

9.10 Bibliographical and Historical Notes 259

9.10.1 More Recent Developments 260

9.11 Problems 261

CHAPTER 10 Boundary Pattern Analysis 266

10.1 Introduction 266

10.2 Boundary Tracking Procedures 269

10.3 Centroidal Profiles 269

10.4 Problems with the Centroidal Profile Approach 270

10.4.1 Some Solutions 271

10.5 The(s,ψ)Plot 274

10.6 Tackling the Problems of Occlusion 276

10.7 Accuracy of Boundary Length Measures 279

10.8 Concluding Remarks 280

10.9 Bibliographical and Historical Notes 281

10.9.1 More Recent Developments 282

10.10 Problems 282

CHAPTER 11 Line Detection 284

11.1 Introduction 284

11.2 Application of the Hough Transform to Line Detection 285

11.3 The Foot-of-Normal Method 288

11.3.1 Application of the Foot-of-Normal Method 290

11.4 Longitudinal Line Localization 290

11.5 Final Line Fitting 292

11.6 Using RANSAC for Straight Line Detection 293

11.7 Location of Laparoscopic Tools 297

11.8 Concluding Remarks 299

11.9 Bibliographical and Historical Notes 300

11.9.1 More Recent Developments 301

11.10 Problems 301

CHAPTER 12 Circle and Ellipse Detection 303

12.1 Introduction 304

12.2 Hough-Based Schemes for Circular Object Detection 305

12.3 The Problem of Unknown Circle Radius 308

12.3.1 Some Practical Results 310

12.4 The Problem ofAccurate Center Location 311

12.4.1 A Solution Requiring Minimal Computation 313

12.5 Overcoming the Speed Problem 314

12.5.1 More Detailed Estimates of Speed 314

12.5.2 Robustness 315

12.5.3 Practical Results 316

12.5.4 Summary 317

12.6 Ellipse Detection 320

12.6.1 The Diameter Bisection Method 320

12.6.2 The Chord-Tangent Method 322

12.6.3 Finding the Remaining Ellipse Parameters 323

12.7 Human Iris Location 325

12.8 Hole Detection 327

12.9 Concluding Remarks 327

12.10 Bibliographical and Historical Notes 328

12.10.1 More Recent Developments 330

12.11 Problems 331

CHAPTER 13 The Hough Transform and Its Nature 333

13.1 Introduction 333

13.2 The Generalized Hough Transform 334

13.3 Setting Up the Generalized Hough Transform—Some Relevant Questions 336

13.4 Spatial Matched Filtering in Images 336

13.5 From Spatial Matched Filters to Generalized Hough Transforms 337

13.6 Gradient Weighting Versus Uniform Weighting 339

13.6.1 Calculation of Sensitivity and Computational Load 339

13.7 Summary 342

13.8 Use of the GHT for Ellipse Detection 343

13.8.1 Practical Details 347

13.9 Comparing the Various Methods 349

13.10 Fast Implementations of the Hough Transform 350

13.11 The Approach of Gerig and Klein 352

13.12 Concluding Remarks 353

13.13 Bibliographical and Historical Notes 354

13.13.1 More Recent Developments 356

13.14 Problems 357

CHAPTER 14 Pattern Matching Techniques 358

14.1 Introduction 359

14.2 A Graph-Theoretic Approach to Object Location 359

14.2.1 A Practical Example―Locating Cream Biscuits 363

14.3 Possibilities for Saving Computation 366

14.4 Using the Generalized Hough Transform for Feature Collation 369

14.4.1 Computational Load 370

14.5 Generalizing the Maximal Clique and Other Approaches 371

14.6 Relational Descriptors 373

14.7 Search 376

14.8 Concluding Remarks 377

14.9 Bibliographical and Historical Notes 378

14.9.1 More Recent Developments 380

14.10 Problems 381

PART 3 3-D VISION AND MOTION 387

CHAPTER 15 The Three-Dimensional World 389

15.1 Introduction 389

15.2 3-D Vision—the Variety of Methods 390

15.3 Projection Schemes for Three-Dimensional Vision 392

15.3.1 Binocular Images 393

15.3.2 The Correspondence Problem 396

15.4 Shape from Shading 398

15.5 Photometric Stereo 402

15.6 The Assumption of Surface Smoothness 405

15.7 Shape from Texture 407

15.8 Use of Structured Lighting 408

15.9 Three-Dimensional Object Recognition Schemes 410

15.10 Horaud's Junction Orientation Technique 411

15.11 An Important Paradigm―Location of Industrial Parts 415

15.12 Concluding Remarks 417

15.13 Bibliographical and Historical Notes 419

15.13.1 More Recent Developments 420

15.14 Problems 421

CHAPTER 16 Tackling the Perspective n-point Problem 424

16.1 Introduction 424

16.2 The Phenomenon of Perspective Inversion 425

16.3 Ambiguity of Pose under Weak Perspective Projection 427

16.4 Obtaining Unique Solutions to the Pose Problem 430

16.4.1 Solution of the Three-Point Problem 433

16.4.2 Using Symmetric Trapezia for Estimating Pose 434

16.5 Concluding Remarks 434

16.6 Bibliographical and Historical Notes 436

16.6.1 More Recent Developments 437

16.7 Problems 438

CHAPTER 17 Invariants and Perspective 439

17.1 Introduction 440

17.2 Cross-ratios:the“Ratio of Ratios”Concept 441

17.3 Invariants for Noncollinear Points 445

17.3.1 Further Remarks About the Five-Point Configuration 447

17.4 Invariants for Points on Conics 449

17.5 Differential and Semi-difierential Invariants 452

17.6 Symmetric Cross-ratio Functions 454

17.7 Vanishing Point Detection 456

17.8 More on Vanishing Points 458

17.9 Apparent Centers of Circles and Ellipses 460

17.10 The Route to Face Recognition 462

17.10.1 The Face as Part of a 3-D Object 464

17.11 Perspective Effects in Art and Photography 466

17.12 Concluding Remarks 472

17.13 Bibliographical and Historical Notes 474

17.13.1 More Recent Developments 475

17.14 Problems 475

CHAPTER 18 Image Transformations and Camera Calibration 478

18.1 Introduction 479

18.2 Image Transformations 479

18.3 Camera Calibration 483

18.4 Intfinsic and Extrinsic Parameters 486

18.5 Correcting for Radial Distortions 488

18.6 Multiple View Vision 490

18.7 Generalized Epipolar Geometry 491

18.8 The Essential Matrix 492

18.9 The Fundamental Matrix 495

18.10 Properties of the Essential and Fundamental Matrices 496

18.11 Estimating the Fundamental Matrix 497

18.12 An Update on the Eight-Point Algorithm 497

18.13 Image Rectification 498

18.14 3-D Reconstruction 499

18.15 Concluding Remarks 501

18.16 Bibliographical and Historical Notes 502

18.16.1 More Recent Developments 503

18.17 Problems 504

CHAPTER 19 Motion 505

19.1 Introduction 505

19.2 Optical Flow 506

19.3 Interpretation of Optical Flow Fields 509

19.4 Using Focus of Expansion to Avoid Collision 511

19.5 Time-to-Adjacency Analysis 513

19.6 Basic Difficulties with the Optical Flow Model 514

19.7 Stereo from Motion 515

19.8 The Kalman Filter 517

19.9 Wide Baseline Matching 519

19.10 Concluding Remarks 521

19.11 Bibliographical and Historical Notes 522

19.12 Problem 522

PART 4 TOWARD REAL-TIME PATTERN RECOGN ITION SYSTEMS 523

CHAPTER 20 Automated Visual Inspection 525

20.1 Introduction 525

20.2 The Process of Inspection 527

20.3 The Types of Object to be Inspected 527

20.3.1 Food Products 528

20.3.2 Precision Components 528

20.3.3 Differing Requirements for Size Measurement 529

20.3.4 Three-Dimensional Objects 530

20.3.5 Other Products and Materials for Inspection 530

20.4 Summary:The Main Categories of Inspection 530

20.5 Shape Deviations Relative to a Standard Template 532

20.6 Inspection of Circular Products 533

20.7 Inspection of Printed Circuits 537

20.8 Steel Strip and Wood Inspection 538

20.9 Inspection of Products with High Levels of Variability 539

20.10 X-Ray Inspection 542

20.10.1 The Dual-Energy Approach to X-Ray Inspection 546

20.11 The Importance of Color in Inspection 546

20.12 Bringing Inspection to the Factory 548

20.13 Concluding Remarks 549

20.14 Bibliographical and Historical Notes 550

20.14.1 More Recent Developments 552

CHAPTER 21 Inspection of Cereal Grains 553

21.1 Introduction 553

21.2 Case Study:Location of Dark Contaminants in Cereals 554

21.2.1 Application of Morphological and Nonlinear Filters to Locate Rodent Droppings 555

21.2.2 Problems with Closing 558

21.2.3 Ergot Detection Using the Global Valley Method 558

21.3 Case Study:Location of Insects 560

21.3.1 The Vectorial Strategy for Linear Feature Detection 560

21.3.2 Designing Linear Feature Detection Masks for Larger Windows 563

21.3.3 Application to Cereal Inspection 564

21.3.4 Experimental Resuits 564

21.4 Case Study:High-Speed Grain Location 566

21.4.1 Extending an Earlier Sampling Approach 566

21.4.2 Application to Grain Inspection 567

21.4.3 Summary 571

21.5 Optimizing the Output for Sets of Directional Template Masks 572

21.5.1 Application of the Formulae 573

21.5.2 Discussion 574

21.6 Concluding Remarks 575

21.7 Bibliographical and Historical Notes 575

21.7.1 More Recent Developments 576

CHAPTER 22 Surveillance 578

22.1 Introduction 579

22.2 Surveillance—The Basic Geometry 580

22.3 Foreground―Background Separation 584

22.3.1 Background Modeling 585

22.3.2 Practical Examples of Background Modeling 591

22.3.3 Direct Detection of the Foreground 593

22.4 Particle Filters 594

22.5 Use of Color Histograms for Tracking 600

22.6 Implementation of Particle Filters 604

22.7 Chamfer Matching,Tracking,and Occlusion 607

22.8 Combining Views from Multiple Cameras 609

22.8.1 The Case of Nonoverlapping Fields of View 613

22.9 Applications to the Monitoring of Traffic Flow 614

22.9.1 The System of Bascle et al 614

22.9.2 The System of Koller et al 616

22.10 License Plate Location 619

22.11 Occlusion Classification for Tracking 621

22.12 Distinguishing Pedestrians by Their Gait 623

22.13 Human Gait Analysis 627

22.14 Model-Based Tracking of Animals 629

22.15 Concluding Remarks 631

22.16 Bibliographical and Historical Notes 632

22.16.1 More Recent Developments 634

22.17 Problem 635

CHAPTER 23 In-Vehicle Vision Systems 636

23.1 Introduction 637

23.2 Locating the Roadway 638

23.3 Location of Road Markings 640

23.4 Location of Road Signs 641

23.5 Location of Vehicles 645

23.6 Information Obtained by Viewing License Plates and Other Structural Features 647

23.7 Locating Pedestrians 651

23.8 Guidance and Egomotion 653

23.8.1 A Simple Path Planning Algorithm 656

23.9 Vehicle Guidance in Agriculture 656

23.9.1 3-D Aspects of the Task 660

23.9.2 Real-Time Implementation 661

23.10 Concluding Remarks 662

23.11 More Detailed Developments and Bibliographies Relating to Advanced Driver Assistance Systems 663

23.11.1 Developments in Vehicle Detection 664

23.11.2 Developments in Pedestrian Detection 666

23.11.3 Developments in Road and Lane Detection 668

23.11.4 Developments in Road Sign Detection 669

23.11.5 Developments in Path Planning,Navigation, and Egomotion 671

23.12 Problem 671

CHAPTER 24 Statistical Pattern Recognition 672

24.1 Introduction 673

24.2 The Nearest Neighbor Algorithm 674

24.3 Bayes'Decision Theory 676

24.3.1 The Naive Bayes'Classifier 678

24.4 Relation of the Nearest Neighbor and Bayes' Approaches 679

24.4.1 Mathematical Statement of the Problem 679

24.4.2 The Importance of the Nearest Neighbor Classifier 681

24.5 The Optimum Number of Features 681

24.6 Cost Functions and Error-Reject Tradeoff 682

24.7 The Receiver Operating Characteristic 684

24.7.1 On the Variety of Performance Measures Relating to Error Rates 686

24.8 Multiple Classifiers 688

24.9 Cluster Analysis 691

24.9.1 Supervised and Unsupervised Learning 691

24.9.2 Clustering Procedures 692

24.10 Principal Components Analysis 695

24.11 The Relevance of Probability in Image Analysis 699

24.12 Another Look at Statistical Pattern Recognition:The Support Vector Machine 700

24.13 Artificial Neural Networks 701

24.14 The Back-Propagation Algorithm 705

24.15 MLP Architectures 708

24.16 Overfitting to the Training Data 709

24.17 Concluding Remarks 712

24.18 Bibliographical and Historical Notes 713

24.18.1 More Recent Developments 715

24.19 Problems 717

CHAPTER 25 Image Acquisition 718

25.1 Introduction 718

25.2 Illumination Schemes 719

25.2.1 Eliminating Shadows 721

25.2.2 Principles for Producing Regions of Uniform Illumination 724

25.2.3 Case of Two Infinite Parallel Strip Lights 726

25.2.4 Overview of the Uniform Illumination Scenario 729

25.2.5 Use of Line-Scan Cameras 730

25.2.6 Light Emitting Diode(LED)Sources 731

25.3 Cameras and Digitization 732

25.3.1 Digitization 734

25.4 The Sampling Theorem 735

25.5 Hyperspectral Imaging 738

25.6 Concluding Remarks 739

25.7 Bibliographical and Historical Notes 740

25.7.1 More Recent Developments 741

CHAPTER 26 Real-Time Hardware and Systems Design Considerations 742

26.1 Introduction 743

26.2 Parallel Processing 744

26.3 SIMD Systems 745

26.4 The Gain in Speed Attainable with N Processors 747

26.5 Flynn's Classification 748

26.6 Optimal Implementation of Image Analysis Algorithms 750

26.6.1 Hardware Specification and Design 751

26.6.2 Basic Ideas on Optimal Hardware Implementation 752

26.7 Some Useful Real-Time Hardware Options 754

26.8 Systems Design Considerations 755

26.9 Design of Inspection Systems―the Status Quo 757

26.10 System Optimization 760

26.11 Concluding Remarks 761

26.12 Bibliographical and Historical Notes 763

26.12.1 General Background 763

26.12.2 Developments Since 2000 764

26.12.3 More Recent Developments 765

CHAPTER 27 Epilogue―Perspectivesin Vision 767

27.1 Introduction 767

27.2 Parameters of Importance in Machine Vision 768

27.3 Tradeoffs 770

27.3.1 Some Important Tradeoffs 770

27.3.2 Tradeoffs for Two-Stage Template Matching 771

27.4 Moore's Law in Action 772

27.5 Hardware,Algorithms,and Processes 773

27.6 The Importance of Choice of Representation 774

27.7 Past,Present,and Future 775

27.8 Bibliographical and Historical Notes 777

Appendix A Robust Statistics 778

References 796

Author Index 845

Subject Index 861