《计算机视觉算法与应用》深度解析：核心技术与实践

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"《计算机视觉：算法与应用》是由Richard Szeliski撰写的一本专业书籍，深入探讨了计算机视觉领域的核心算法和技术。本书是计算机科学系列的一部分，由David Gries和Fred B. Schneider共同担任系列编辑，旨在为读者提供一个全面的视角，涵盖了从基础原理到实际应用的广泛内容。作为高清原著，它配备了详细的标签，便于读者快速定位所需的知识点。书中的主题包括图像处理、特征检测和描述、物体识别、光流分析、三维重建、计算机立体视觉、深度学习在计算机视觉中的应用等。作者Richard Szeliski以其在计算机视觉领域的深厚背景，结合丰富的实践经验，向读者展示了如何设计和实现这些关键算法，以及它们在诸如自动驾驶、医疗影像分析、安防监控等现实生活场景中的重要角色。每一章都详尽地介绍了理论概念、数学模型、以及实用代码示例，帮助读者理解算法的工作原理，并通过实践提高解决问题的能力。此外，书中还讨论了计算机视觉的发展历史，以及未来可能的研究方向，让读者能够跟上这个快速发展的领域动态。本书不仅适合计算机科学专业的学生和研究人员，也对工程师、数据科学家和机器学习从业者有着重要的参考价值。对于那些希望通过计算机视觉技术解决实际问题的读者，无论是为了学术研究还是职业发展，都能从中找到丰富的资源和宝贵的经验分享。最后，值得注意的是，根据版权规定，除非获得Springer出版社或版权所有者事先书面许可，否则复制或传播本书内容需遵循特定条款。如果你有兴趣获取更详细的复制权信息，应直接联系出版社，确保遵循法律要求。"

Contents

4.3.3 Vanishing points ............................224

4.3.4 Application: Rectangle detection ....................226

4.4 Additional reading ...............................227

4.5 Exercises ....................................228

5 Segmentation 235

5.1 Active contours .................................237

5.1.1 Snakes .................................238

5.1.2 Dynamic snakes and CONDENSATION ................243

5.1.3 Scissors .................................246

5.1.4 Level Sets ................................248

5.1.5 Application: Contour tracking and rotoscoping ............249

5.2 Split and merge .................................250

5.2.1 Watershed ................................251

5.2.2 Region splitting (divisive clustering) ..................251

5.2.3 Region merging (agglomerative clustering) ..............251

5.2.4 Graph-based segmentation .......................252

5.2.5 Probabilistic aggregation ........................253

5.3 Mean shift and mode ﬁnding ..........................254

5.3.1 K-means and mixtures of Gaussians ..................256

5.3.2 Mean shift ...............................257

5.4 Normalized cuts .................................260

5.5 Graph cuts and energy-based methods .....................264

5.5.1 Application: Medical image segmentation ...............268

5.6 Additional reading ...............................268

5.7 Exercises ....................................270

6 Feature-based alignment 273

6.1 2D and 3D feature-based alignment ......................275

6.1.1 2D alignment using least squares ....................275

6.1.2 Application: Panography ........................277

6.1.3 Iterative algorithms ...........................278

6.1.4 Robust least squares and RANSAC ..................281

6.1.5 3D alignment ..............................283

6.2 Pose estimation .................................284

6.2.1 Linear algorithms ............................284

6.2.2 Iterative algorithms ...........................286

6.2.3 Application: Augmented reality ....................287

6.3 Geometric intrinsic calibration .........................288

6.3.1 Calibration patterns ...........................289

6.3.2 Vanishing points ............................290

6.3.3 Application: Single view metrology ..................292

6.3.4 Rotational motion ...........................293

6.3.5 Radial distortion ............................295

6.4 Additional reading ...............................296

6.5 Exercises

....................................296

Contents

7 Structure from motion 303

7.1 Triangulation ..................................305

7.2 Two-frame structure from motion ........................307

7.2.1 Projective (uncalibrated) reconstruction ................312

7.2.2 Self-calibration .............................313

7.2.3 Application: View morphing ......................315

7.3 Factorization ..................................315

7.3.1 Perspective and projective factorization ................318

7.3.2 Application: Sparse 3D model extraction ...............319

7.4 Bundle adjustment ...............................320

7.4.1 Exploiting sparsity ...........................322

7.4.2 Application: Match move and augmented reality ...........324

7.4.3 Uncertainty and ambiguities ......................326

7.4.4 Application: Reconstruction from Internet photos ...........327

7.5 Constrained structure and motion ........................329

7.5.1 Line-based techniques .........................330

7.5.2 Plane-based techniques .........................331

7.6 Additional reading ...............................332

7.7 Exercises ....................................332

8 Dense motion estimation 335

8.1 Translational alignment .............................337

8.1.1 Hierarchical motion estimation .....................341

8.1.2 Fourier-based alignment ........................341

8.1.3 Incremental reﬁnement .........................345

8.2 Parametric motion ................................350

8.2.1 Application: Video stabilization ....................354

8.2.2 Learned motion models .........................354

8.3 Spline-based motion ..............................355

8.3.1 Application: Medical image registration ................358

8.4 Optical ﬂow ...................................360

8.4.1 Multi-frame motion estimation .....................363

8.4.2 Application: Video denoising .....................364

8.4.3 Application: De-interlacing ......................364

8.5 Layered motion .................................365

8.5.1 Application: Frame interpolation ....................368

8.5.2 Transparent layers and reﬂections ...................368

8.6 Additional reading ...............................370

8.7 Exercises ....................................371

9 Image stitching 375

9.1 Motion models .................................378

9.1.1 Planar perspective motion

.......................379

9.1.2 Application: Whiteboard and document scanning ...........379

9.1.3 Rotational panoramas ..........................380

9.1.4 Gap closing ...............................382

xvi

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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