图书介绍
Computer Vision:Algorithms and Applicationspdf电子书版本下载
- Szeliski 著
- 出版社： Springer;Central Book Services [Distributor]
- ISBN：9781848829343;1848829345
- 出版时间：2010
- 标注页数：812页
- 文件大小：430MB
- 文件页数：831页
- 主题词：
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图书目录
1 Introduction 1
1.1 What is computer vision？ 3
1.2 A brief history 10
1.3 Book overview 17
1.4 Sample syllabus 23
1.5 A note on notation 25
1.6 Additional reading 25
2 Image formation 27
2.1 Geometric primitives and transformations 29
2.1.1 Geometric primitives 29
2.1.2 2D transformations 33
2.1.3 3D transformations 36
2.1.4 3D rotations 37
2.1.5 3D to 2D projections 42
2.1.6 Lens distortions 52
2.2 Photometric image formation 54
2.2.1 Lighting 54
2.2.2 Reflectance and shading 55
2.2.3 Optics 61
2.3 The digital camera 65
2.3.1 Sampling and aliasing 69
2.3.2 Color 71
2.3.3 Compression 80
2.4 Additional reading 82
2.5 Exercises 82
3 Image processing 87
3.1 Point operators 89
3.1.1 Pixel transforms 91
3.1.2 Color transforms 92
3.1.3 Compositing and matting 92
3.1.4 Histogram equalization 94
3.1.5 Application：Tonal adjustment 97
3.2 Linear filtering 98
3.2.1 Separable liltering 102
3.2.2 Examples of linear filtering 103
3.2.3 Band-pass and steerable filters 104
3.3 More neighborhood operators 108
3.3.1 Non-linear filtering 108
3.3.2 Morphology 112
3.3.3 Distance transforms 113
3.3.4 Connected components 115
3.4 Fourier transforms 116
3.4.1 Fourier transform pairs 119
3.4.2 Two-dimensional Fourier transforms 123
3.4.3 Wiener filtering 123
3.4.4 Application：Sharpening，blur，and noise removal 126
3.5 Pyramids and wavelets 127
3.5.1 Interpolation 127
3.5.2 Decimation 130
3.5.3 Multi-resolution representations 132
3.5.4 Wavelets 136
3.5.5 Application：Image blending 140
3.6 Geometric transformations 143
3.6.1 Parametric transformations 145
3.6.2 Mesh-based warping 149
3.6.3 Application：Feature-based morphing 152
3.7 Global optimization 153
3.7.1 Regularization 154
3.7.2 Markov random fields 158
3.7.3 Application：Image restoration 169
3.8 Additional reading 169
3.9 Exercises 171
4 Feature detection and matching 181
4.1 Points and patches 183
4.1.1 Feature detectors 185
4.1.2 Feature descriptors 196
4.1.3 Feature matching 200
4.1.4 Feature tracking 207
4.1.5 Application：Performance-driven animation 209
4.2 Edges 210
4.2.1 Edge detection 210
4.2.2 Edge linking 215
4.2.3 Application：Edge editing and enhancement 219
4.3 Lines 220
4.3.1 Successive approximation 220
4.3.2 Hough transforms 221
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
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
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
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
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
A.1.3 QR factorization 649
A.1.4 Cholesky factorization 650
A.2 Linear least squares 651
A.2.1 Total least squares 653
A.3 Non-linear least squares 654
A.4 Direct sparse matrix techniques 655
A.4.1 Variable reordering 656
A.5 Iterative techniques 656
A.5.1 Conjugate gradient 657
A.5.2 Preconditioning 659
A.5.3 Multigrid 660
B Bayesian modeling and inference 661
B.1 Estimation theory 662
B.1.1 Likelihood for multivariate Gaussian noise 663
B.2 Maximum likelihood estimation and least squares 665
B.3 Robust statistics 666
B.4 Prior models and Bayesian inference 667
B.5 Markov random fields 668
B.5.1 Gradient descent and simulated annealing 670
B.5.2 Dynamic programming 670
B.5.3 Belief propagation 672
B.5.4 Graph cuts 674
B.5.5 Linear programming 676
B.6 Uncertainty estimation （error analysis） 678
C Supplementary material 679
C.1 Data sets 680
C.2 Software 682
C.3 Slides and lectures 689
C.4 Bibliography 690
References 691
Index 793
1 Introduction 1
2 Image formation 27
3 Image processing 87
4 Feature detection and matching 181
5 Segmentation 235
6 Feature-based alignment 273
7 Structure from motion 303
8 Dense motion estimation 335
9 Image stitching 375
10 Computational photography 409
11 Stereo correspondence 467
12 3D reconstruction 505
13 Image-based rendering 543
14 Recognition 575