1. 1 motivation 1

1.2 Why is computer vision difficult 2

1.3 image expression and image analysis task 4

1.4 Summary 7

1.5 Reference 7

Chapter II Images, Their Expression and Properties 8

2. 1 Expressed some concepts vividly 8

Continuous image function 8

2.2 image digitization 10

2.2. 1 sampling 10

2.2.2 Quantization 1 1

2.3 digital image properties 12

2.3. 1 digital image metric sum

Topological attribute 12

Histogram 16

2.3.3 Entropy 17

2.3.4 Visual perception of images 18

Image quality 20

2.3.6 Noise in the image

2.4 Color Images 22

2.4. 1 color physics 22

2.4.2 Color perceived by people 23

2.4.3 Color Space 26

Palette image 28

2.4.5 Color constancy 28

2.5 Camera Overview 29

2.5. 1 photosensitive sensor 29

2.5.2 Black and white camera 30

2.5.3 Color camera 32

2.6 Summary 33

2.7 References 34

Chapter 3 Images and Their Mathematical and Physical Background 35

3. 1 overview 35

3. 1. 1 linear 35

3. 1.2 Dirac distribution sum

Convolution 35

3.2 Integral Linear Transformation 37

3.2. 1 as an image of the linear system 37

3.2.2 Introduction to Integral Linear Transformation 37

3.2.3 1D Fourier transform 38

3.2.4 2D Fourier Transform 4 1

3.2.5 Sampling and Shannon Limit 43

Discrete Cosine Transform 46

Wavelet transform 47

Feature analysis 5 1

Singular value decomposition 52

3.2. 10 principal component analysis 53

3. 2. 1 1 other orthogonal image transformations 54

3.3 Image as a Random Process 55

3.4 Physics of image formation 57

3.4. 1 as the image of radiation measurement 57

3.4.2 Image Acquisition and Geometrical Optics 57

3.4.3 Lens Aberration and Radial Distortion 60

3.4.4 Image acquisition from the perspective of radiology 62

Surface reflection 64

3.5 Summary 67

3.6 References 67

Chapter 4 Data Structure of Image Analysis 69

4. 1 hierarchy of image data representation 69

4.2 Traditional image data structure 70

4.2. 1 matrix 70

Chain 72

Topological data structure 73

Relationship structure 73

4.3 Hierarchical Data Structure 74

4.3. 1 Pyramid 74

Quadtree 75

4.3.3 Other pyramid structures 76

4.4 Summary 77

4.5 References 78

The fifth chapter image preprocessing 79

5. 1 pixel brightness transformation 79

5. 1. 1 position-related brightness correction 80

5. 1.2 gray scale transformation 80

5.2 Geometric transformation 82

5.2. 1 pixel coordinate transformation 83

5.2.2 Brightness Interpolation 84

5.3 Local Pretreatment 86

Image smoothing 86

5.3.2 Edge detection operator 92

5.3.3 Zero crossing of second derivative 96

5.3.4 Proportion in image processing 98

5.3.5 Canny Edge Extraction 100

Parametric edge model 102

5.3.7 Edge in Multispectral Images 103

5.3.8 Frequency domain local preprocessing 103

5.3.9 Using local preprocessing operators

Line detection 108

5.3. 10 corner (point of interest) detection 109

5. 3. 1 1 maximum stability limit area detection 1 12

5.4 Image Restoration 1 14

5.4. 1 easily recoverable degeneration 1 14

5.4.2 Inverse filtering 1 15

Wiener filter 1 15

5.5 Summary 1 17

5.6 Reference 1 18

The first part of chapter 6 124

6. 1 threshold 124

6. 1. 1 threshold detection method 126

6. 1.2 optimal threshold 127

6. 1.3 Multispectral Threshold 129

6.2 Edge-based segmentation 130

6.2. 1 threshold of edge image 13 1

6.2.2 Edge Relaxation Method 133

Boundary tracking 135

6.2.4 Edge Tracking as Graphic Search 139

6.2.5 Edge Tracking as Dynamic Programming 146

6.2.6 Hough transform 149

6.2.7 Using boundary position information

Boundary detection 155

6.2.8 Construction of 156 area from the border.

6.3 Region-based Segmentation 157

6.3. 1 The area was merged into 158.

6.3.2 Regional Division 160

6.3.3 Split and merge 16 1

6.3.4 watershed division 163

6.3.5 Post-processing of regional growth 166

6.4 Matching 166

6.4. 1 matching standard 167

6.4.2 Matching control strategy 168

6.5 Evaluation of Sub-paragraph 169

6.5. 1 supervision and evaluation 169

6.5.2 Unsupervised Assessment 172

6.6 Summary 172

6.7 Reference 175

Chapter 7, Part 2 182

7. 1 mean shift segmentation 182

7.2 Active Contour Model -Snake 187

7.2. 1 classic snakes and balloons 188

7.2.2 Extension 19 1

7.2.3 gradient vector flow snake 19 1

7.3 Geometric Deformation Model-Level Set Sum

Geodetic active contour 194

7.4 Fuzzy Connectivity 200

7.5 Face to 3D Image Segmentation 204

7.5. 1 simultaneous detection of boundary pairs 205

7.5.2 Excellent surface inspection 208

7.6 graph cut segmentation 209

7.7 Optimal Single and Multi-surface Segmentation 2 14

7.8 Summary 223

7.9 References 224

Chapter 8 Shape Representation and Description 232

8. 1 region ID 234

8.2 Shape representation and description based on contour 236

8.2. 1 chain code 237

8.2.2 Simple Geometric Boundary Representation 237

8.2.3 Boundary Fourier Transform 239

8.2.4 Use the boundary description of fragment sequence 24 1

B-spline representation 243

8.2.6 Other contour-based shapes

Describe method 245

8.2.7 Shape unchanged 245

8.3 Based on the shape representation and description of region 248

8.3. 1 Simple Scalar Region Description 248

8.3.2 Torque 25 1

Convex shell 253

8.3.4 Graphical representation based on regional skeleton 257

Domain decomposition 259

8.3.6 This area is adjacent to Figure 260.

8.4 Shape Category 26 1

8.5 Summary 26 1

8.6 References 263

Chapter 9 Object Identification 270

9. 1 knowledge representation 270

9.2 Statistical Pattern Recognition 274

9.2. 1 classification principle 275

Classifier settings 276

Classifier learning 278

9.2.4 Support Vector Machine 280

9.2.5 Cluster analysis 284

9.3 Neural Network 286

9.3. 1 feedforward network 287

9.3.2 Unsupervised Learning 288

9.3.3 Hopfield neural network 289

9.4 Syntactic Pattern Recognition 290

Grammar and Language 29 1

Grammar analysis and grammar classifier 293

9.4.3 Syntactic classification learning and

Grammar derivation 294

9.5 Recognition as Pattern Matching 295

Isomorphism of graphs and subgraphs 296

9.5.2 Similarity of charts 298

9.6 Optimization Technology in Identification 299

9.6. 1 genetic algorithm 300

Simulated annealing 302

9.7 Fuzzy System 303

9.7. 1 fuzzy set and fuzzy membership function 304

9.7.2 Fuzzy Set Operation 305

Fuzzy reasoning 306

9.7.4 Fuzzy System Design and Training 308

9.8 Enhancement Method in Pattern Recognition 309

9.9 Summary 3 1 1

9 9. 10/0Reference 3 14

Chapter 10 Image Understanding 3 19

10. 1 image understanding control strategy 320

10. 1. 1 parallel and serial processing control 320

10. 1.2 hierarchical control 32 1

10. 1.3 bottom-up control 32 1

Model-based control 32 1

10. 1.5 hybrid control strategy 322

10. 1.6 Non-hierarchical control 325

10.2 RANSAC: consistent by random sampling.

Suitable for 326

10.3 point distribution model 329

10.4 activity apparent model 337

10.5 Pattern Recognition Method in Image Comprehension 344

10.5. 1 classification-based segmentation 344

10.5.2 Context Image Classification 346

10.6 fast lifting cascade classifier

Object detection 349

10.7 scene annotation and constraint propagation 352

10.7. 1 discrete relaxation method 353

10.7.2 probability relaxation method 355

10.7.3 Search Interpretation Tree 357

10.8 semantic image segmentation and understanding 357

10.8. 1 semantic area increased by 358.

10.8.2 gene image interpretation 360

10.9 Hidden Markov Model 365

10.9. 1 application 369

10.9.2 coupled HMM 370

10.9.3 Bayesian belief network 37 1

10. 10 Gaussian mixture model and expectation maximization 372

10. 1 1 summary 378

10. 12 Reference 380

Chapter 1 1 3 d vision and geometry 389

1 1. 1 3D vision task 389

Mar theory 39 1

1 1. 1.2 Other visual categories: active and.

A clear vision 392

1 1.2 basis of projective geometry 393

1 1.2. 1 Points and Hyperplanes in Projective Space3994

1 1.2.2 should be 395.

1 1.2.3 estimating homography according to corresponding points 397

1 1.3 single-view camera 400

1 1.3. 1 camera model 400

1 1.3.2 Projection sum in homogeneous coordinate system

Back projection 402

1 1.3.3 Calibrate one from a known scene.

Camera 403

1 1.4 to reconstruct the scene 403 from multiple views.

1 1.4. 1 triangulation 403

1 1.4.2 projective reconstruction 404

1 1.4.3 matching constraint 405

1 1.4.4 beam adjustment method 406

1 1.4.5 Upgrade projection reconstruction and

Self-calibration 407

1 1.5 Dual cameras and stereo perception 408

1 1.5. 1 polar geometry-basic

Matrix 408

1 1.5.2 Relative motion of camera

-basic matrix 4 10

1 1.5.3 decompose the basic matrix into

Camera matrix 4 1 1

1 1.5.4 Estimate the basic matrix 4 1 1 from the corresponding point.

1 1.5.5 Dual camera calibration structure 4 12

1 1.5.6 revised calculation 4 14

1 1.6 Three cameras and three visual tensors 4 15

1 1.6. 1 stereo corresponding point algorithm 4 17

1 1.6.2 Actively collect range images 42 1

1 1.7 3D information of radiation measurement 423

1 1.7. 1 From Shadow to Shape 423

1 1.7.2 photometric stereo vision18000.100000000805

1 1.8 Summary 427

1 1.9 reference 428

Chapter 12 Application of 3D Vision 433

12. 1

12. 1. 1 from motion to shape 433

12. 1.2 from texture to shape 437

12. 1.3 others from x to shape

439 technology

12.2 complete 3D object 440

12.2. 1 3D objects, models and

relevant issues

12.2.2 marking 44 1

12.2.3 Volume Representation and Direct Measurement 443

12.2.4 batch modeling strategy 444

12.2.5 surface modeling strategy 446

12.2.6 is used to obtain a complete 3D model.

Panel Labeling and Fusion 447

12.3 Vision based on 3D model

12.3. 1 is generally considered as 45 1.

The drive algorithm 452

12.3.3 model-based brightness image

Surface object recognition 455

12.3.4 Distance based on vehicle type

Image recognition 456

12.4 3D view representation of 3D scene 456

12.4. 1 observation space 456

12.4.2 Multi-view representation and chart 457

12.4.3 structured as a 2D view.

Expressed geometry 457

12.4.4 displays the stored 2D view.

3D real-world scene 458

12.5 case study-from unorganized 2D

View set reconstruction 3D 460

12.6 Summary 463

12.7 Reference 464

Chapter 13 Mathematical Morphology 470

Basic concepts of morphology 470

13.2 four principles of morphology 47 1

13.3 binary expansion and corrosion 472

1 inflation 472

Corrosion 474

13.3.3 hit-miss transition 476

13.3.4 open operation and close operation 476

13.4 Gray expansion and corrosion 477

13.4. 1 top surface, shadow, gray level

Expansion and corrosion 477

13.4.2 umbra homeomorphism theorem and expansion,

Corrosion and opening and closing operations

Property 479

13.4.3 Top hat reconstruction 480

13.5 bone and object markers 48 1

13. 5. 1 homotopy transformation 8 1

13.5.2 skeleton and maximum ball 48 1

13.5.3 refinement, coarsening and homotopy skeleton 482

13.5.4 extinction function and final corrosion 485

13.5.5 Final Corrosion and Distance Function 486

13.5.6 geodetic transformation 487

13.5.7 morphological reconstruction 488

13.6 particle size determination method 489

13.7 morphological segmentation and watershed 49 1

13.7. 1 particle segmentation, labeling and summation

Watershed 49 1

13.7.2 binary morphological segmentation 49 1

13.7.3 Gray Level Segmentation and Watershed 493

13.8 Summary 494

13.9 Reference 495

Chapter 14 Image data compression 497

14. 1 image data attribute 498

14.2 discretization in image data compression

Image conversion 498

14.3 predictive compression method 500

14.4 vector quantization 502

14.5 hierarchical progressive compression method 502

Comparison of 14.6 compression methods 503

14.7 Other technologies 504

14.8 code 504

14.9 JPEG and MPEG image compression 505

14. 9. 1 JPEG- still image

Compression 505

14. 9. 2 JPEG 2000 compression 506

14. 9. 3 MPEG- full dynamic

Video compression 508

14. 10 Summary 509

Reference 5 1 1

Chapter 15 Texture 5 14

15. 1 statistical texture description 5 16

15. 1. 1 method based on spatial frequency

15. 1.2 *** Generating Matrix 5 17

15. 1.3 edge frequency 5 19

15. 1.4 Element length (strokes) 520

15. 1.5 law texture energy metric 52 1

15. 1.6 fractal texture description 52 1

15. 1.7 Multi-scale texture description

Wavelet domain method 522

15. 1.8 described by other textures.

Statistical method 525

15.2 Syntactic texture description method 526

15.2. 1 shape chain syntax 526

Graph grammar 527

15.2.3 layered texture

Original packet 528

15.3 hybrid texture description method 530

Application of 15.4 texture recognition method

15.5 Summary 53 1

15.6 Reference 532

Chapter 16 Motion analysis 537

16. 1 differential analysis method 539

16.2 optical flow 542

Optical flow calculation 542

16.2.2 global and local optical flow estimation

16.2.3 local and global integration

Optical flow estimation 546

Optical flow in motion analysis 16.2.4/kloc-0 /40666.000000060686

16.3 Analysis based on correspondence of points of interest 549

16. 3. 1 detection of interest points120066.100006868686

16.3.2 point of interest correspondence 549

16.4 detection of specific motion patterns 55 1

16.5 video tracking 554

Background modeling 554

16.5.2 Tracking based on kernel function 558

16.5.3 target path analysis 562

16.6 auxiliary tracking motion model 566

Kalman filter 567

The particulate filter 570

16.7 Summary 573

16.8 references 575

Vocabulary 58 1