图像处理、分析与机器视觉 英文版PDF电子书下载

1 Introduction 1

1.1 Summary 8

1.2 Exercises 8

1.3 References 9

2 The digitized image and its properties 10

2.1 Basic concepts 10

2.1.1 Image functions 10

2.1.3 The Fourier transform 13

2.1.2 The Dirac distribution and convolution 13

2.1.4 Images as a stochastic process 15

2.1.5 Images as linear systems 17

2.2 Image digitization 18

2.2.1 Sampling 18

2.2.2 Quantization 22

2.2.3 Color images 23

2.3 Digital image properties 27

2.3.1 Metric and topological properties of digital images 27

2.3.2 Histograms 32

2.3.3 Visual perception of the image 33

2.3.4 Image quality 35

2.3.5 Noise in images 35

2.4 Summary 37

2.5 Exercises 38

2.6 References 40

3 Data structures for image analysis 42

3.1 Levels of image data representation 42

3.2 Traditional image data structures 43

3.2.1 Matrices 43

3.2.2 Chains 45

3.2.3 Topological data structures 47

3.2.4 Relational structures 48

3.3 Hierarchical data structures 49

3.3.1 Pyamids 49

3.3.2 Quadtrees 51

3.3.3 Other pyramidical structures 52

3.4 Summary 53

3.5 Exercises 54

3.6 References 55

4 Image pre-processing 57

4.1 Pixel brightness transformations 58

4.1.1 Position-dependent brightness correction 58

4.1.2 Gray-scale transformation 59

4.2 Geometric transformations 62

4.2.1 Pixel co-ordinate transformations 63

4.2.2 Brightness interpolation 65

4.3 Local pre-processing 68

4.3.1 Image smoothing 69

4.3.2 Edge detectors 77

4.3.3 Zero-crossings of the second derivative 83

4.3.4 Scale in image processing 88

4.3.5 Canny edge detection 90

4.3.6 Parametric edge models 93

4.3.7 Edges in multi-spectral images 94

4.3.8 Other local pre-processing operators 94

4.3.9 Adaptive neighborthood pre-processing 98

4.4 Image restoration 102

4.4.1 Degradations that are easy to restore 105

4.4.3 Wiener filtration 106

4.4.2 Inverse filtration 106

4.5 Summary 108

4.6 Exercises 111

4.7 References 118

5 Segmentation 123

5.1 Thresholding 124

5.1.1 Threshold detection methods 127

5.1.2 Optimal thresholding 128

5.1.3 Multi-spectral thresholding 131

5.1.4 Thresholding in hierarchical data structures 133

5.2 Edge-based segmentation 134

5.2.1 Edge image thresholding 135

5.2.2 Edge relaxation 137

5.2.3 Border tracing 142

5.2.4 Border detection as graph searching 148

5.2.5 Border detection as dynamic programming 158

5.2.6 Hough transforms 163

5.2.7 Border detection using border location information 173

5.2.8 Region construction form borders 174

5.3 Region-based segmentation 176

5.3.1 Region merging 177

5.3.2 Region splitting 181

5.3.3 Splitting and merging 181

5.3.4 Watershed segmentation 186

5.3.5 Region growing post-processing 188

5.4 Matching 190

5.4.1 Matching criteria 191

5.4.2 Control strategies of matching 193

5.5.1 Simultaneous detection of border pairs 194

5.5 Advanced optimal border and surface detection approaches 194

5.5.2 Surface detection 199

5.6 Summary 205

5.7 Exercises 210

5.8 References 216

6 Shape representation and description 228

6.1 Region identification 232

6.2 Contour-based shape representation and description 235

6.2.1 Chain codes 236

6.2.2 Simple geometric border representation 237

6.2.3 Fourier transforms of boundaries 240

6.2.4 Boundary description using segment sequences 242

6.2.5 B-spline representation 245

6.2.6 Other contour-based shape description approaches 248

6.2.7 Spape invariants 249

6.3 Region-based shape representation and description 254

6.3.1 Simple scalar region descriptors 254

6.3.2 Moments 259

6.3.3 Convex hull 262

6.3.4 Graph representation based on region skeleton 267

6.3.5 Region decomposition 271

6.3.6 Region neighborhood graphs 272

6.4 Shape classes 273

6.5 Summary 274

6.6 Exercises 276

6.7 References 279

7 Object recognition 290

7.1 Knowledge representation 291

7.2 Statistical pattern recognition 297

7.2.1 Classification principles 298

7.2.2 Classifier setting 300

7.2.3 Classifier learning 303

7.2.4 Cluster analysis 307

7.3 Neural nets 308

7.3.1 Feed-forward networks 310

7.3.2 Unsupervised learning 312

7.3.3 Hopfield neural nets 313

7.4 Syntactic pattern recognition 315

7.4.1 Grammars and languages 317

7.4.2 Syntactic analysis, syntactic classifier 319

7.4.3 Syntactic classifier learning, grammar inference 321

7.5 Recognition as graph matching 323

7.5.1 Isomorphism of graphs and sub-graphs 324

7.5.2 Similarity of graphs 328

7.6 Optimization techniques in recognition 328

7.6.1 Genetic algorithms 330

7.6.2 Simulated annealing 333

7.7.1 Fuzzy sets and fuzzy membership functions 336

7.7 Fuzzy systems 336

7.7.2 Fuzzy set operators 338

7.7.3 Fuzzy reasoning 339

7.7.4 Fuzzy system design and training 343

7.8 Summary 344

7.9 Exercises 347

7.10 References 354

8 Image understanding 362

8.1.2 Hierarchical control 364

8.1.1 Parallel and serial processing control 364

8.1 Image understanding control strategies 364

8.1.3 Bottom-up control strategies 365

8.1.4 Model-based control strategies 366

8.1.5 Combined control strategies 367

8.1.6 Non-hierarchical control 371

8.2 Active contour models-snakes 374

8.3 Point distribution models 380

8.4 Pattern recognition methods in image understanding 390

8.4.1 Contextual image classification 392

8.5 Scene labeliing and constraint propagation 397

8.5.1 Discrete relaxation 398

8.5.2 Probabilistic relaxation 400

8.5.3 Searching interpretation trees 404

8.6 Semantic image segmentation and understanding 404

8.6.1 Semantic region growing 406

8.6.2 Genetic image interpretation 408

8.7 Hidden Markov models 417

8.8 Summary 423

8.9 Exercises 426

8.10 References 428

9 3D vision, geometry, and radiometry 441

9.1 3D vision tasks 442

9.1.1 Marr s theory 444

9.1.2 Other vision paradigms: Active and purposive vision 446

9.2 Geometry for 3D vision 448

9.2.1 Basic of projective geometry 448

9.2.2 The single perspective camera 449

9.2.3 An overview of single camera calibration 453

9.2.4 Calibration of one camera from a known scene 455

9.2.5 Two cameras, stereopsis 457

9.2.6 The geometry of two cameras；the fundamental matrix 460

9.2.7 Relative motion of the camera；the essential matrix 462

9.2.8 Fundamental matrix estimation from image point correspondences 464

9.2.9 Applications of epipolar geometry in vision 466

9.2.10 Three and more cameras 471

9.2.11 Stereo correspondence algorithms 476

9.2.12 Active acquisition of range images 483

9.3 Radiometry and 3D vision 486

9.3.1 Radiometric considerations in determining gary-level 486

9.3.2 Surface reflectance 490

9.3.3 Shape from shading 494

9.3.4 Photometric stereo 498

9.4 Summary 499

9.5 Exercises 501

9.6 References 502

10 Use of 3D vision 508

10.1 Shape from X 508

10.1.1 Shape from motion 508

10.1.2 Shape from texture 515

10.1.3 Other shape from X techniques 517

10.2 Full 3D objects 519

10.2.1 3D objects, models, and related issues 519

10.2.2 Line labeling 521

10.2.3 Volumetirc representation, direct mesurements 523

10.2.4 Volumetric modeling strategies 525

10.2.5 Surface modeling strategies 527

10.2.6 Registering surface patches and their fusion to get a full 3D model 529

10.3.1 General considerations 535

10.3 3D model-based vision 535

10.3.2 Goad s algorithm 537

10.3.3 Model-based recognition of curved objects from intensity images 541

10.3.4 Model-based recognition based on range images 543

10.4 2D view-based representations of a 3D scene 544

10.4.1 Viewing space 544

10.4.2 Multi-view representations and aspect graphs 544

10.4.3 Geons as a 2D view-based structural representation 545

10.4.4 Visualizing 3D real-world scenes using stored collections of 2D view 546

10.5 Summary 551

10.6 Exercies 552

10.7 References 553

11 Mathematical morphology 559

11.1 Basic morphological concepts 559

11.2 Four morphological principles 561

11.3 Binary dilation and erosion 563

11.3.1 Dilation 563

11.3.2 Erosion 565

11.3.3 Hit-or-miss transformation 568

11.3.4 Opening and closing 568

11.4 Gray-scle dilation and erosion 569

11.4.1 Top surface, umbra, and gray-scale dilation and erosion 570

11.4.2 Umbra homeomorphism theorem, properties of erosion and dilation,opening and closing 573

11.4.3 Top hat transformation 574

11.5 Skeltons and object marking 576

11.5.1 Homotopic transformations 576

11.5.2 Skeletion, maximal ball 576

11.5.3 Thinning, thickening,and homotopic skeleton 578

11.5.4 Quench function, ultimate erosion 581

11.5.5 Ultimate erosion and distance functions 584

11.5.6 Geodesic trandformations 585

11.5.7 Morphological reconstruction 586

11.6 Granulometry 589

11.7 Morphological segmentation and watersheds 590

11.7.1 Particles segmenttation, marking, and watersheds 590

11.7.2 Binary morphological segmentation 592

11.7.3 Gary-scale segmentation, watersheds 594

11.8 Summary 595

11.9 Exercises 597

11.10 References 598

12 Linear discrete image tranforms 600

12.1 Basic theory 600

12.2 Fourier transform 602

12.3 Hadamard transform 604

12.4 Discrete cosine transform 605

12.5 Wavelets 606

12.6 Other orthogonal image transforms 608

12.7 Applications of discrete image transforms 609

12.8 Summary 613

12.9 Exercises 617

12.10 References 619

13.Image data compression 621

13.1 Image data properties 622

13.2 Discrete image tranforms in image data compression 623

13.3 Predictive compression methods 624

13.4 Vector quantization 629

13.5 Hierarchical and progressive compression methods 630

13.6 Comparison of compression methods 631

13.7 Other techniques 632

13.8 Coding 633

13.9 JPEG and MPEG image compression 634

13.9.1 JPEG-still image compression 634

13.9.2 MPEG-full-motion video compression 636

13.10 Summary 637

13.11 Exercises 640

13.12 References 641

14 Texture 646

14.1 Statistical texture description 649

14.1.1 Methods based on spatial frequencies 649

14.1.2 Co-occurrence matices 651

14.1.3 Edge frequency 653

14.1.4 Primitive length (run length) 655

14.1.5 Laws texture energy measures 656

14.1.6 Fractal texture description 657

14.1.7 Other statistical methods of texture description 659

14.2 Syntactic texture description methods 660

14.2.1 Shape chain grammars 661

14.2.2 Graph grammars 663

14.2.3 Primitive grouping in hierarchical textures 664

14.3 Hybrid texture description methods 666

14.4 Texture recognition method applications 667

14.5 Summary 668

14.6 Exercises 670

14.7 References 672

15 Motion analysis 679

15.1 Differential mition analysis methods 682

15.2 Optical flow 685

15.2.1 Optical flow computation 686

15.2.2 Global and local optical fow estimation 689

15.2.3 Optical flow computation approaches 690

15.2.4 Optical flow in motion analysis 693

15.3 Analysis based on correspondence of interest points 696

15.3.1 Detection of interest points 696

15.3.2 Correspondence of interest points 697

15.3.3 Object tracking 700

15.4 Kalman filters 708

15.4.1 Example 709

15.5 Summary 710

15.6 Exercises 712

15.7 References 714

16 Case studies 722

16.1 An optical music recognition system 722

16.2 Automated image analysis in cardiology 727

16.2.1 Robust analysis of coronry angiograms 730

16.2.2 Knowledge-based analysis of intra-vascular ultrasound 733

16.3 Automated indentification of airway trees 738

16.4 Passive surveillance 744

16.5 References 750

Index 755