机器视觉入门：图像处理与分析

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"《图像处理分析与机器视觉》第四版是一本经典的入门教材，专为那些希望在机器视觉领域建立扎实数学和理论基础的学习者设计。该书强调概念讲解而非过度依赖编程代码，特别适合对图像处理技术感兴趣的初学者。作者米兰·索尔卡（Milan Sonka）来自爱荷华大学，瓦克拉夫·赫拉瓦克（Vaclav Hlavac）来自捷克查理斯大学，而罗杰·博伊尔（Roger Boyle）则来自阿伯里斯特威尔大学，三位专家共同编著，确保了内容的专业性和深度。书中涵盖了广泛的图像处理基础知识，包括但不限于图像获取、预处理、特征提取、数字信号处理、图像增强、形态学操作、以及机器视觉的基本原理如模板匹配、边缘检测、特征描述符和目标识别等。读者将通过理论讲解和实例解析，逐步理解并掌握图像数据的分析方法和机器如何理解和处理这些信息。值得注意的是，由于电子版权的限制，书中可能存在部分内容被暂时屏蔽。编辑审阅认为这些缺失的内容不会严重影响整体学习体验，但Cengage Learning保留随时根据后续版权规定调整或移除额外内容的权利。尽管如此，这并不妨碍读者获取核心的理论知识和实践经验。《图像处理分析与机器视觉》第四版是一本理想的资源，对于想要踏入机器视觉领域的学生、研究人员或者工程师来说，它既是理论指导，也是实践引导，有助于提升读者在图像处理领域的技能和理解。无论是为了学术研究还是实际应用，这本书都是一个值得深入学习和探索的起点。"

6.5 Evaluation issues in segmentation 236

6.5.1 Supervised evaluation 237

6.5.2 Unsupervised evaluation 240

6.6 Summary 241

6.7 Exercises 245

6.8 References 248

7 Segmentation II 255

7.1 Mean shift segmentation 255

7.2 Active contour models—snakes 263

7.2.1 Traditional snakes and balloons 264

7.2.2 Extensions 267

7.2.3 Gradient vector ﬂow snakes 268

7.3 Geometric deformable models—level sets and geodesic active contours 273

7.4 Fuzzy connectivity 280

7.5 Towards 3D graph-based image segmentation 288

7.5.1 Simultaneous detection of border pairs 289

7.5.2 Suboptimal surface detection 293

7.6 Graph cut segmentation 295

7.7 Optimal single and multiple surface segmentation— LOGISMOS 303

7.8 Summary 317

7.9 Exercises 319

7.10 References 321

8 Shape representation and description 329

8.1 Region identiﬁcation 333

8.2 Contour-based shape representation and description 335

8.2.1 Chain codes 336

8.2.2 Simple geometric border representation 337

8.2.3 Fourier transforms of boundaries 341

8.2.4 Boundary description using segment sequences 343

8.2.5 B-spline representation 346

8.2.6 Other contour-based shape description approaches 348

8.2.7 Shape invariants 349

8.3 Region-based shape representation and description 353

8.3.1 Simple scalar region descriptors 353

8.3.2 Moments 358

8.3.3 Convex hull 360

8.3.4 Graph representation based on region skeleton 365

8.3.5 Region decomposition 370

8.3.6 Region neighborhood graphs 372

8.4 Shape classes 373

8.5 Summary 373

8.6 Exercises 375

8.7 References 379

9 Object recognition 385

9.1 Knowledge representation 386

Contentsxiv

Copyright 201 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).

Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

9.2 Statistical pattern recognition 390

9.2.1 Classiﬁcation principles 392

9.2.2 Nearest neighbors 393

9.2.3 Classiﬁer setting 395

9.2.4 Classiﬁer learning 398

9.2.5 Support vector machines 400

9.2.6 Cluster analysis 406

9.3 Neural nets 407

9.3.1 Feed-forward networks 409

9.3.2 Unsupervised learning 411

9.3.3 Hopﬁeld neural nets 412

9.4 Syntactic pattern recognition 413

9.4.1 Grammars and languages 415

9.4.2 Syntactic analysis, syntactic classiﬁer 417

9.4.3 Syntactic classiﬁer learning, grammar inference 420

9.5 Recognition as graph matching 421

9.5.1 Isomorphism of graphs and subgraphs 421

9.5.2 Similarity of graphs 425

9.6 Optimization techniques in recognition 426

9.6.1 Genetic algorithms 427

9.6.2 Simulated annealing 430

9.7 Fuzzy systems 432

9.7.1 Fuzzy sets and fuzzy membership functions 432

9.7.2 Fuzzy set operators 434

9.7.3 Fuzzy reasoning 435

9.7.4 Fuzzy system design and training 438

9.8 Boosting in pattern recognition 439

9.9 Random forests 442

9.9.1 Random forest training 444

9.9.2 Random forest decision making 446

9.9.3 Random forest extensions 448

9.10 Summary 448

9.11 Exercises 452

9.12 References 459

10 Image understanding 464

10.1 Image understanding control strategies 466

10.1.1 Parallel and serial processing control 466

10.1.2 Hierarchical control 466

10.1.3 Bottom-up control 467

10.1.4 Model-based control 468

10.1.5 Combined control 469

10.1.6 Non-hierarchical control 472

10.2 SIFT: Scale invariant feature transform 474

10.3 RANSAC: Fitting via random sample consensus 477

10.4 Point distribution models 481

10.5 Active appearance models 492

10.6 Pattern recognition methods in image understanding 503

Contents xv

10.6.1 Classiﬁcation-based segmentation 503

10.6.2 Contextual image classiﬁcation 505

10.6.3 Histograms of oriented gradients—HOG 509

10.7 Boosted cascades of classiﬁers 513

10.8 Image understanding using random forests 517

10.9 Scene labeling and constraint propagation 524

10.9.1 Discrete relaxation 525

10.9.2 Probabilistic relaxation 527

10.9.3 Searching interpretation trees 530

10.10 Semantic image segmentation and understanding 531

10.10.1 Semantic region growing 532

10.10.2 Genetic image interpretation 534

10.11 Hidden Markov models 543

10.11.1 Applications 548

10.11.2 Coupled HMMs 549

10.11.3 Bayesian belief networks 551

10.12 Markov random ﬁelds 553

10.12.1 Applications to images and vision 555

10.13 Gaussian mixture models and expectation–maximization 556

10.14 Summary 564

10.15 Exercises 568

10.16 References 572

11 3D geometry, correspondence, 3D from intensities 582

11.1 3D vision tasks 583

11.1.1 Marr’s theory 585

11.1.2 Other vision paradigms: Active and purposive vision 587

11.2 Basics of projective geometry 589

11.2.1 Points and hyperplanes in projective space 590

11.2.2 Homography 592

11.2.3 Estimating homography from point correspondences 594

11.3 A single perspective camera 598

11.3.1 Camera model 598

11.3.2 Projection and back-projection in homogeneous coordinates 601

11.3.3 Camera calibration from a known scene 602

11.4 Scene reconstruction from multiple views 602

11.4.1 Triangulation 603

11.4.2 Projective reconstruction 604

11.4.3 Matching constraints 605

11.4.4 Bundle adjustment 607

11.4.5 Upgrading the projective reconstruction, self-calibration 608

11.5 Two cameras, stereopsis 609

11.5.1 Epipolar geometry; fundamental matrix 610

11.5.2 Relative motion of the camera; essential matrix 612

11.5.3 Decomposing the fundamental matrix to camera matrices 613

11.5.4 Estimating the fundamental matrix from point correspondences 614

11.5.5 Rectiﬁed conﬁguration of two cameras 615

11.5.6 Computing rectiﬁcation 617

xvi Contents

11.6 Three cameras and trifocal tensor 619

11.6.1 Stereo correspondence algorithms 621

11.6.2 Active acquisition of range images 627

11.7 3D information from radiometric measurements 630

11.7.1 Shape from shading 631

11.7.2 Photometric stereo 635

11.8 Summary 636

11.9 Exercises 637

11.10 References 639

12 Use of 3D vision 644

12.1 Shape from X 644

12.1.1 Shape from motion 644

12.1.2 Shape from texture 651

12.1.3 Other shape from X techniques 652

12.2 Full 3D objects 655

12.2.1 3D objects, models, and related issues 655

12.2.2 Line labeling 656

12.2.3 Volumetric representation, direct measurements 658

12.2.4 Volumetric modeling strategies 660

12.2.5 Surface modeling strategies 662

12.2.6 Registering surface patches and their fusion to get a full 3D

model 663

12.3 2D view-based representations of a 3D scene 670

12.3.1 Viewing space 670

12.3.2 Multi-view representations and aspect graphs 670

12.4 3D reconstruction from an unorganized set of 2D views, and Structure

from Motion 671

12.5 Reconstructing scene geometry 674

12.6 Summary 677

12.7 Exercises 678

12.8 References 680

13 Mathematical morphology 684

13.1 Basic morphological concepts 684

13.2 Four morphological principles 686

13.3 Binary dilation and erosion 687

13.3.1 Dilation 688

13.3.2 Erosion 689

13.3.3 Hit-or-miss transformation 692

13.3.4 Opening and closing 692

13.4 Gray-scale dilation and erosion 694

13.4.1 Top surface, umbra, and gray-scale dilation and erosion 694

13.4.2 Umbra homeomorphism theorem, properties of erosion and

dilation, opening and closing 697

13.4.3 Top hat transformation 698

13.5 Skeletons and object marking 699

13.5.1 Homotopic transformations 699

Contents xvii

13.5.2 Skeleton, medial axis, maximal ball 699

13.5.3 Thinning, thickening, and homotopic skeleton 701

13.5.4 Quench function, ultimate erosion 704

13.5.5 Ultimate erosion and distance functions 706

13.5.6 Geodesic transformations 707

13.5.7 Morphological reconstruction 709

13.6 Granulometry 711

13.7 Morphological segmentation and watersheds 713

13.7.1 Particle segmentation, marking, and watersheds 713

13.7.2 Binary morphological segmentation 714

13.7.3 Gray-scale segmentation, watersheds 716

13.8 Summary 717

13.9 Exercises 718

13.10 References 720

14 Image data compression 722

14.1 Image data properties 723

14.2 Discrete image transforms in image data compression 724

14.3 Predictive compression methods 727

14.4 Vector quantization 730

14.5 Hierarchical and progressive compression methods 730

14.6 Comparison of compression methods 732

14.7 Other techniques 733

14.8 Coding 733

14.9 JPEG and MPEG image compression 734

14.9.1 JPEG—still image compression 734

14.9.2 JPEG–2000 compression 736

14.9.3 MPEG—full-motion video compression 738

14.10 Summary 740

14.11 Exercises 742

14.12 References 744

15 Texture 747

15.1 Statistical texture description 750

15.1.1 Methods based on spatial frequencies 750

15.1.2 Co-occurrence matrices 752

15.1.3 Edge frequency 754

15.1.4 Primitive length (run length) 755

15.1.5 Laws’ texture energy measures 757

15.1.6 Local binary patterns—LBPs 757

15.1.7 Fractal texture description 762

15.1.8 Multiscale texture description—wavelet domain approaches 764

15.1.9 Other statistical methods of texture description 768

15.2 Syntactic texture description methods 769

15.2.1 Shape chain grammars 770

15.2.2 Graph grammars 772

15.2.3 Primitive grouping in hierarchical textures 773

xviii Contents

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