Computer Vision:Algorithms and Applications2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载

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1 Introduction1

1.1 What is computer vision？3

1.2 A brief history10

1.3 Book overview17

1.4 Sample syllabus23

1.5 A note on notation25

1.6 Additional reading25

2 Image formation27

2.1 Geometric primitives and transformations29

2.1.1 Geometric primitives29

2.1.2 2D transformations33

2.1.3 3D transformations36

2.1.4 3D rotations37

2.1.5 3D to 2D projections42

2.1.6 Lens distortions52

2.2 Photometric image formation54

2.2.1 Lighting54

2.2.2 Reflectance and shading55

2.2.3 Optics61

2.3 The digital camera65

2.3.1 Sampling and aliasing69

2.3.2 Color71

2.3.3 Compression80

2.4 Additional reading82

2.5 Exercises82

3 Image processing87

3.1 Point operators89

3.1.1 Pixel transforms91

3.1.2 Color transforms92

3.1.3 Compositing and matting92

3.1.4 Histogram equalization94

3.1.5 Application：Tonal adjustment97

3.2 Linear filtering98

3.2.1 Separable liltering102

3.2.2 Examples of linear filtering103

3.2.3 Band-pass and steerable filters104

3.3 More neighborhood operators108

3.3.1 Non-linear filtering108

3.3.2 Morphology112

3.3.3 Distance transforms113

3.3.4 Connected components115

3.4 Fourier transforms116

3.4.1 Fourier transform pairs119

3.4.2 Two-dimensional Fourier transforms123

3.4.3 Wiener filtering123

3.4.4 Application：Sharpening，blur，and noise removal126

3.5 Pyramids and wavelets127

3.5.1 Interpolation127

3.5.2 Decimation130

3.5.3 Multi-resolution representations132

3.5.4 Wavelets136

3.5.5 Application：Image blending140

3.6 Geometric transformations143

3.6.1 Parametric transformations145

3.6.2 Mesh-based warping149

3.6.3 Application：Feature-based morphing152

3.7 Global optimization153

3.7.1 Regularization154

3.7.2 Markov random fields158

3.7.3 Application：Image restoration169

3.8 Additional reading169

3.9 Exercises171

4 Feature detection and matching181

4.1 Points and patches183

4.1.1 Feature detectors185

4.1.2 Feature descriptors196

4.1.3 Feature matching200

4.1.4 Feature tracking207

4.1.5 Application：Performance-driven animation209

4.2 Edges210

4.2.1 Edge detection210

4.2.2 Edge linking215

4.2.3 Application：Edge editing and enhancement219

4.3 Lines220

4.3.1 Successive approximation220

4.3.2 Hough transforms221

4.3.3 Vanishing points224

4.3.4 Application：Rectangle detection226

4.4 Additional reading227

4.5 Exercises228

5 Segmentation235

5.1 Active contours237

5.1.1 Snakes238

5.1.2 Dynamic snakes and CONDENSATION243

5.1.3 Scissors246

5.1.4 Level Sets248

5.1.5 Application：Contour tracking and rotoscoping249

5.2 Split and merge250

5.2.1 Watershed251

5.2.2 Region splitting （divisive clustering）251

5.2.3 Region merging （agglomerative clustering）251

5.2.4 Graph-based segmentation252

5.2.5 Probabilistic aggregation253

5.3 Mean shift and mode finding254

5.3.1 K-means and mixtures of Gaussians256

5.3.2 Mean shift257

5.4 Normalized cuts260

5.5 Graph cuts and energy-based methods264

5.5.1 Application：Medical image segmentation268

5.6 Additional reading268

5.7 Exercises270

6 Feature-based alignment273

6.1 2D and 3D feature-based alignment275

6.1.1 2D alignment using least squares275

6.1.2 Application：Panography277

6.1.3 Iterative algorithms278

6.1.4 Robust least squares and RANSAC281

6.1.5 3D alignment283

6.2 Pose estimation284

6.2.1 Linear algorithms284

6.2.2 Iterative algorithms286

6.2.3 Application：Augmented reality287

6.3 Geometric intrinsic calibration288

6.3.1 Calibration patterns289

6.3.2 Vanishing points290

6.3.3 Application：Single view metrology292

6.3.4 Rotational motion293

6.3.5 Radial distortion295

6.4 Additional reading296

6.5 Exercises296

7 Structure from motion303

7.1 Triangulation305

7.2 Two-frame structure from motion307

7.2.1 Projective （uncalibrated） reconstruction312

7.2.2 Self-calibration313

7.2.3 Application：View morphing315

7.3 Factorization315

7.3.1 Perspective and projective factorization318

7.3.2 Application：Sparse 3D model extraction319

7.4 Bundle adjustment320

7.4.1 Exploiting sparsity322

7.4.2 Application：Match move and augmented reality324

7.4.3 Uncertainty and ambiguities326

7.4.4 Application：Reconstruction from Internet photos327

7.5 Constrained structure and motion329

7.5.1 Line-based techniques330

7.5.2 Plane-based techniques331

7.6 Additional reading332

7.7 Exercises332

8 Dense motion estimation335

8.1 Translational alignment337

8.1.1 Hierarchical motion estimation341

8.1.2 Fourier-based alignment341

8.1.3 Incremental refinement345

8.2 Parametric motion350

8.2.1 Application：Video stabilization354

8.2.2 Learned motion models354

8.3 Spline-based motion355

8.3.1 Application：Medical image registration358

8.4 Optical flow360

8.4.1 Multi-frame motion estimation363

8.4.2 Application：Video denoising364

8.4.3 Application：De-interlacing364

8.5 Layered motion365

8.5.1 Application：Frame interpolation368

8.5.2 Transparent layers and reflections368

8.6 Additional reading370

8.7 Exercises371

9 Image stitching375

9.1 Motion models378

9.1.1 Planar perspective motion379

9.1.2 Application：Whiteboard and document scanning379

9.1.3 Rotational panoramas380

9.1.4 Gap closing382

9.1.5 Application：Video summarization and compression383

9.1.6 Cylindrical and spherical coordinates385

9.2 Global alignment387

9.2.1 Bundle adjustment388

9.2.2 Parallax removal391

9.2.3 Recognizing panoramas392

9.2.4 Direct vs.feature-based alignment393

9.3 Compositing396

9.3.1 Choosing a compositing surface396

9.3.2 Pixel selection and weighting （de-ghosting）398

9.3.3 Application：Photomontage403

9.3.4 Blending403

9.4 Additional reading406

9.5 Exercises407

10 Computational photography409

10.1 Photometric calibration412

10.1.1 Radiometric response function412

10.1.2 Noise level estimation415

10.1.3 Vignetting416

10.1.4 Optical blur （spatial response） estimation416

10.2 High dynamic range imaging419

10.2.1 Tone mapping427

10.2.2 Application：Flash photography434

10.3 Super-resolution and blur removal436

10.3.1 Color image demosaicing440

10.3.2 Application：Colorization442

10.4 Image matting and compositing443

10.4.1 Blue screen matting445

10.4.2 Natural image matting446

10.4.3 Optimization-based matting450

10.4.4 Smoke，shadow，and flash matting452

10.4.5 Video matting454

10.5 Texture analysis and synthesis455

10.5.1 Application：Hole filling and inpainting457

10.5.2 Application：Non-photorealistic rendering458

10.6 Additional reading460

10.7 Exercises461

11 Stereo correspondence467

11.1 Epipolar geometry471

11.1.1 Rectification472

11.1.2 Plane sweep474

11.2 Sparse correspondence475

11.2.1 3D curves and profiles476

11.3 Dense correspondence477

11.3.1 Similarity measures479

11.4 Local methods480

11.4.1 Sub-pixel estimation and uncertainty482

11.4.2 Application：Stereo-based head tracking483

11.5 Global optimization484

11.5.1 Dynamic programming485

11.5.2 Segmentation-based techniques487

11.5.3 Application：Z-keying and background replacement489

11.6 Multi-view stereo489

11.6.1 Volumetric and 3D surface reconstruction492

11.6.2 Shape from silhouettes497

11.7 Additional reading499

11.8 Exercises500

12 3D reconstruction505

12.1 Shape from X508

12.1.1 Shape from shading and photometric stereo508

12.1.2 Shape from texture510

12.1.3 Shape from focus511

12.2 Active rangefinding512

12.2.1 Range data merging515

12.2.2 Application：Digital heritage517

12.3 Surface representations518

12.3.1 Surface interpolation518

12.3.2 Surface simplification520

12.3.3 Geometry images520

12.4 Point-based representations521

12.5 Volumetric representations522

12.5.1 Implicit surfaces and level sets522

12.6 Model-based reconstruction523

12.6.1 Architecture524

12.6.2 Heads and faces526

12.6.3 Application：Facial animation528

12.6.4 Whole body modeling and tracking530

12.7 Recovering texture maps and albedos534

12.7.1 Estimating BRDFs536

12.7.2 Application：3D photography537

12.8 Additional reading538

12.9 Exercises539

13 Image-based rendering543

13.1 View interpolation545

13.1.1 View-dependent texture maps547

13.1.2 Application：Photo Tourism548

13.2 Layered depth images549

13.2.1 Impostors，sprites，and layers549

13.3 Light fields and Lumigraphs551

13.3.1 Unstructured Lumigraph554

13.3.2 Surface light fields555

13.3.3 Application：Concentric mosaics556

13.4 Environment mattes556

13.4.1 Higher-dimensional light fields558

13.4.2 The modeling to rendering continuum559

13.5 Video-based rendering560

13.5.1 Video-based animation560

13.5.2 Video textures561

13.5.3 Application：Animating pictures564

13.5.4 3D Video564

13.5.5 Application：Video-based walkthroughs566

13.6 Additional reading569

13.7 Exercises570

14 Recognition575

14.1 Object detection578

14.1.1 Face detection578

14.1.2 Pedestrian detection585

14.2 Face recognition588

14.2.1 Eigenfaces589

14.2.2 Active appearance and 3D shape models596

14.2.3 Application：Personal photo collections601

14.3 Instance recognition602

14.3.1 Geometric alignment603

14.3.2 Large databases604

14.3.3 Application：Location recognition609

14.4 Category recognition611

14.4.1 Bag of words612

14.4.2 Part-based models615

14.4.3 Recognition with segmentation620

14.4.4 Application：Intelligent photo editing621

14.5 Context and scene understanding625

14.5.1 Learning and large image collections627

14.5.2 Application：Image search630

14.6 Recognition databases and test sets631

14.7 Additional reading631

14.8 Exercises637

15 Conclusion641

A Linear algebra and numerical techniques645

A.1 Matrix decompositions646

A.1.1 Singular value decomposition646

A.1.2 Eigenvalue decomposition647

A.1.3 QR factorization649

A.1.4 Cholesky factorization650

A.2 Linear least squares651

A.2.1 Total least squares653

A.3 Non-linear least squares654

A.4 Direct sparse matrix techniques655

A.4.1 Variable reordering656

A.5 Iterative techniques656

A.5.1 Conjugate gradient657

A.5.2 Preconditioning659

A.5.3 Multigrid660

B Bayesian modeling and inference661

B.1 Estimation theory662

B.1.1 Likelihood for multivariate Gaussian noise663

B.2 Maximum likelihood estimation and least squares665

B.3 Robust statistics666

B.4 Prior models and Bayesian inference667

B.5 Markov random fields668

B.5.1 Gradient descent and simulated annealing670

B.5.2 Dynamic programming670

B.5.3 Belief propagation672

B.5.4 Graph cuts674

B.5.5 Linear programming676

B.6 Uncertainty estimation （error analysis）678

C Supplementary material679

C.1 Data sets680

C.2 Software682

C.3 Slides and lectures689

C.4 Bibliography690

References691

Index793

1 Introduction1

2 Image formation27

3 Image processing87

4 Feature detection and matching181

5 Segmentation235

6 Feature-based alignment273

7 Structure from motion303

8 Dense motion estimation335

9 Image stitching375

10 Computational photography409

11 Stereo correspondence467

12 3D reconstruction505

13 Image-based rendering543

14 Recognition575