计算机视觉：算法与应用

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"Computer Vision Richard Szeliski" 是一本由Richard Szeliski博士编著的计算机视觉领域的经典著作，全面介绍了计算机视觉的算法和应用。计算机视觉是计算机科学的一个重要分支，它涉及如何使机器“看”并理解图像和视频数据。Richard Szeliski的这本书深入探讨了这一领域的核心概念、技术和理论。书中的内容可能涵盖了以下几个关键知识点： 1. **图像处理基础**：包括图像获取、颜色模型、图像增强和复原，以及图像分析的基础方法，这些都是计算机视觉系统处理输入数据的第一步。 2. **几何计算**：涉及相机模型、投影几何、立体视觉和多视图几何，这些是理解图像间关系和恢复场景三维结构的关键。 3. **特征检测与描述**：如角点检测、边缘检测、SIFT、SURF等特征，它们用于识别图像中的关键点和描述符，对于物体识别和匹配至关重要。 4. **机器学习与模式识别**：书中可能会讨论支持向量机（SVM）、神经网络和深度学习在分类、检测和识别任务中的应用，尤其是随着深度学习的兴起，这部分内容尤为重要。 5. **运动估计与跟踪**：研究如何从连续帧中估计物体或摄像机的运动，这对于视频分析和增强现实应用很有用。 6. **三维重建**：包括从单视图、多视图或激光扫描数据中重建场景的几何结构，这是计算机视觉中的一个重要挑战。 7. **语义理解**：涉及到场景理解、对象识别、场景分类和语义分割，这是让计算机理解图像内容的关键步骤。 8. **应用实例**：可能包括自动驾驶、无人机导航、医学成像、人脸识别、图像搜索、虚拟现实和增强现实等多个领域的真实应用案例。 9. **版权与法律问题**：书中提到的版权法律信息提醒读者在使用计算机视觉技术时，要遵守相关的版权法规，特别是在复制或传播内容时。 "Computer Vision Richard Szeliski" 为读者提供了一个全面的计算机视觉知识体系，适合学生、研究人员和从业人员作为参考教材或自学资料，帮助他们理解和开发实际的计算机视觉系统。

Contents

9.1.5 Application: Video summarization and compression .........383

9.1.6 Cylindrical and spherical coordinates .................385

9.2 Global alignment ................................387

9.2.1 Bundle adjustment ...........................388

9.2.2 Parallax removal ............................391

9.2.3 Recognizing panoramas ........................392

9.2.4 Direct vs. feature-based alignment ...................393

9.3 Compositing ...................................396

9.3.1 Choosing a compositing surface ....................396

9.3.2 Pixel selection and weighting (de-ghosting) ..............398

9.3.3 Application: Photomontage ......................403

9.3.4 Blending ................................403

9.4 Additional reading ...............................406

9.5 Exercises ....................................407

10 Computational photography 409

10.1 Photometric calibration .............................412

10.1.1 Radiometric response function .....................412

10.1.2 Noise level estimation .........................415

10.1.3 Vignetting ................................416

10.1.4 Optical blur (spatial response) estimation ...............416

10.2 High dynamic range imaging ..........................419

10.2.1 Tone mapping ..............................427

10.2.2 Application: Flash photography ....................434

10.3 Super-resolution and blur removal .......................436

10.3.1 Color image demosaicing .......................440

10.3.2 Application: Colorization .......................442

10.4 Image matting and compositing .........................443

10.4.1 Blue screen matting ...........................445

10.4.2 Natural image matting .........................446

10.4.3 Optimization-based matting ......................450

10.4.4 Smoke, shadow, and ﬂash matting ...................452

10.4.5 Video matting ..............................454

10.5 Texture analysis and synthesis .........................455

10.5.1 Application: Hole ﬁlling and inpainting ................457

10.5.2 Application: Non-photorealistic rendering ...............458

10.6 Additional reading ...............................460

10.7 Exercises ....................................461

11 Stereo correspondence 467

11.1 Epipolar geometry ...............................471

11.1.1 Rectiﬁcation ..............................472

11.1.2 Plane sweep ...............................474

11.2 Sparse correspondence .............................475

11.2.1 3D curves and proﬁles .........................476

11.3 Dense correspondence

.............................477

xvii

Contents

11.3.1 Similarity measures ...........................479

11.4 Local methods ..................................480

11.4.1 Sub-pixel estimation and uncertainty ..................482

11.4.2 Application: Stereo-based head tracking ................483

11.5 Global optimization ...............................484

11.5.1 Dynamic programming .........................485

11.5.2 Segmentation-based techniques ....................487

11.5.3 Application: Z-keying and background replacement ..........489

11.6 Multi-view stereo ................................489

11.6.1 Volumetric and 3D surface reconstruction ...............492

11.6.2 Shape from silhouettes .........................497

11.7 Additional reading ...............................499

11.8 Exercises ....................................500

12 3D reconstruction 505

12.1 Shape from X ..................................508

12.1.1 Shape from shading and photometric stereo ..............508

12.1.2 Shape from texture ...........................510

12.1.3 Shape from focus ............................511

12.2 Active rangeﬁnding ...............................512

12.2.1 Range data merging ..........................515

12.2.2 Application: Digital heritage ......................517

12.3 Surface representations .............................518

12.3.1 Surface interpolation ..........................518

12.3.2 Surface simpliﬁcation .........................520

12.3.3 Geometry images ............................520

12.4 Point-based representations ...........................521

12.5 Volumetric representations ...........................522

12.5.1 Implicit surfaces and level sets .....................522

12.6 Model-based reconstruction ...........................523

12.6.1 Architecture ...............................524

12.6.2 Heads and faces .............................526

12.6.3 Application: Facial animation .....................528

12.6.4 Whole body modeling and tracking ..................530

12.7 Recovering texture maps and albedos .....................534

12.7.1 Estimating BRDFs ...........................536

12.7.2 Application: 3D photography .....................537

12.8 Additional reading ...............................538

12.9 Exercises ....................................539

13 Image-based rendering 543

13.1 View interpolation ................................545

13.1.1 View-dependent texture maps .....................547

13.1.2 Application: Photo Tourism ......................548

13.2 Layered depth images ..............................549

13.2.1 Impostors, sprites, and layers

......................549

xviii

Contents

13.3 Light ﬁelds and Lumigraphs ..........................551

13.3.1 Unstructured Lumigraph ........................554

13.3.2 Surface light ﬁelds ...........................555

13.3.3 Application: Concentric mosaics ....................556

13.4 Environment mattes ...............................556

13.4.1 Higher-dimensional light ﬁelds .....................558

13.4.2 The modeling to rendering continuum .................559

13.5 Video-based rendering .............................560

13.5.1 Video-based animation .........................560

13.5.2 Video textures .............................561

13.5.3 Application: Animating pictures ....................564

13.5.4 3D Video ................................564

13.5.5 Application: Video-based walkthroughs ................566

13.6 Additional reading ...............................569

13.7 Exercises ....................................570

14 Recognition 575

14.1 Object detection .................................578

14.1.1 Face detection .............................578

14.1.2 Pedestrian detection ..........................585

14.2 Face recognition .................................588

14.2.1 Eigenfaces ...............................589

14.2.2 Active appearance and 3D shape models ................596

14.2.3 Application: Personal photo collections ................601

14.3 Instance recognition ...............................602

14.3.1 Geometric alignment ..........................603

14.3.2 Large databases .............................604

14.3.3 Application: Location recognition ...................609

14.4 Category recognition ..............................611

14.4.1 Bag of words ..............................612

14.4.2 Part-based models ...........................615

14.4.3 Recognition with segmentation .....................620

14.4.4 Application: Intelligent photo editing .................621

14.5 Context and scene understanding ........................625

14.5.1 Learning and large image collections .................627

14.5.2 Application: Image search .......................630

14.6 Recognition databases and test sets .......................631

14.7 Additional reading ...............................631

14.8 Exercises ....................................637

15 Conclusion 641

A Linear algebra and numerical techniques 645

A.1 Matrix decompositions .............................646

A.1.1 Singular value decomposition .....................646

A.1.2 Eigenvalue decomposition

.......................647

xix

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计算机视觉：算法与应用

Computer Vision: Algorithms and Applications（Richard Szeliski）

Computer Vision Algorithms and Applications【Richard Szeliski】

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