图像处理、分析与机器视觉：英文版

1　Introduction　1
1.1　Summary　8
1.2　Exercises　8
1.3　References　9
2　The digitized image and its properties　10
2.1　Basic concepts　10
2.1.1　Image functions　10
2.1.2　The Dirac distribution and convolution　13
2.1.3　The Fourier transform　13
2.1.4　Images as a stochastic process　15
2.1.5　Images as linear systems　17
2.2　Image digitization　18
2.2.1　Sampling　18
2.2.2　Quantization　22
2.2.3　Color images　23
2.3　Digital image properties　27
2.3.1　Metric and topological properties of digital images　27
2.3.2　Histograms　32
2.3.3　Visual perception of the image　33
2.3.4　Image quality　35
2.3.5　Noise in images　35
2.4　Summary　37
2.5　Exercises　38
2.6　References　40
3　Data Structures for image analysis　42
3.1　Levels of image data representation　42
3.2　Traditional image data structures　43
3.2.1　Matrices　43
3.2.2　Chains　45
3.2.3　Topological data structures　47
3.2.4　Relational structures　48
3.3　Hierarchical data structures　49
3.3.1　Pyramids　49
3.3.2　Quadtrees　51
3.3.3　Other pyramidical structures　52
3.4　Summary　53
3.5　Exercises　54
3.6　References　55
4　Image pre-processing　57
4.1　Pixel brightness transformations　58
4.1.1　Position-dependent brightness correction　58
4.1.2　Gray-scale transformation　59
4.2　Geometric transformations　62
4.2.1　Pixel co-ordinate transformations　63
4.2.2　Brightness interpolation　65
4.3　Local pre-processing　68
4.3.1　Image smoothing　69
4.3.2　Edge detectors　77
4.3.3　Zero-crossings of the second derivative　83
4.3.4　Scale in image processing　88
4.3.5　Canny edge detection　90
4.3.6　Parametric edge models　93
4.3.7　Edges in multi-spectral images　94
4.3.8　Other local pre-processing operators　94
4.3.9　Adaptive neighborhood pre-processing　98
4.4　Image restoration　102
4.4.1　Degradations that are easy to restore　105
4.4.2　Inverse filtration　106
4.4.3　Wiener filtration　106
4.5　Summary　108
4.6　Exercises　111
4.7　References　118
5　Segmentation　123
5.1　Thresholding　124
5.1.1　Threshold detection methods　127
5.1.2　Optimal thresholding　128
5.1.3　Multi-spectral thresholding　131
5.1.4　Thresholding in hierarchical data structures　133
5.2　Edge-based segmentation　134
5.2.1　Edge image thresholding　135
5.2.2　Edge relaxation　137
5.2.3　Border tracing　142
5.2.4　Border detection as graph searching　148
5.2.5　Border detection as dynamic programming　158
5.2.6　Hough transforms　163
5.2.7　Border detection using border location information　173
5.2.8　Region construction from borders　174
5.3　Region-based segmentation　176
5.3.1　Region merging　177
5.3.2　Region splitting　181
5.3.3　Splitting and merging　181
5.3.4　Watershed segmentation　186
5.3.5　Region growing post-processing　188
5.4　Matching　190
5.4.1　Matching criteria　191
5.4.2　Control strategies of matching　193
5.5　Advanced optimal border and surface detection approaches　194
5.5.1　Simultaneous detection of border pairs　194
5.5.2　Surface detection　199
5.6　Summary　205
5.7　Exercises　210
5.8　References　216
6　Shape representation and description　228
6.1　Region identification　232
6.2　Contour-based shape representation and description　235
6.2.1　Chain codes　236
6.2.2　Simple geometric border representation　237
6.2.3　Fourier transforms of boundaries　240
6.2.4　Boundary description using segment sequences　242
6.2.5　B-spline representation　245
6.2.6　Other contour-based shape description approaches　248
6.2.7　Shape invariants　249
6.3　Region-baed shape representation and description　254
6.3.1　Simple scalar region descriptors　254
6.3.2　Moments　259
6.3.3　Convex hull　262
6.3.4　Graph representation based on region skeleton　267
6.3.5　Region decomposition　271
6.3.6　Region neighborhood graphs　272
6.4　Shape classes　273
6.5　Summary　274
6.6　Exercises　276
6.7　References　279
7　Object recognition　290
7.1　Knowledge representation　291
7.2　Statistical pattern recognition　297
7.2.1　Classification principles 298
7.2.2　Classifier setting　300
7.2.3　Classifier learning　303
7.2.4　Cluster analysis　307
7.3　Neural nets　308
7.3.1　Feed-forward networks　310
7.3.2　Unsupervised learning　312
7.3.3　Hopfield neural nets　313
7.4　Syntactic pattern recognition　315
7.4.1　Grammars and languages　317
7.4.2　Syntactic analysis，syntactic classifier　319
7.4.3　Syntactic classifier learning，grammar inference　321
7.5　Recognition as graph matching　323
7.5.1　Isomorphism of graphs and sub-graphs　324
7.5.2　Similarity of graphs　328
7.6　Optimization techniques in recognition　328
7.6.1　Genetic algorithms　330
7.6.2　Simulated annealing　333
7.7　Fuzzy systems　336
7.7.1　Fuzzy sets and fuzzy membership functions　336
7.7.2　Fuzzy set operators　338
7.7.3　Fuzzy reasoning　339
7.7.4　Fuzzy system design and training　343
7.8　Summary　344
7.9　Exercises　347
7.10　References　354
8　Image understanding　362
8.1　Image understanding control strategies　364
8.1.1　Parallel and serial processing control　364
8.1.2　Hierarchical control　364
8.1.3　Bottom-up control strategies　365
8.1.4　Model-based control　strategies　366
8.1.5　Combined control strategies　367
8.1.6　Non-hierarchical control　371
8.2　Active contour models-snakes　374
8.3　Point distribution models　380
8.4　Pattern recognition methods in image understanding　390
8.4.1　Contextual image classification　392
8.5　Scene labeling and constraint propagation　397
8.5.1　Discrete relaxation　398
8.5.2　Probabilistic relaxation　400
8.5.3　Searching interpretation trees　404
8.6　Semantic image segmentation and understanding　404
8.6.1　Semantic region growing　406
8.6.2　Genetic image interpretation　408
8.7　Hidden Markov models　417
8.8　Summary　423
8.9　Exercises　426
8.10　References　428
9　3D Vision，geometry，and radiometry　441
9.1　3D vision tasks　442
9.1.1　Marr's theory　444
9.1.2　Other vision paradigms：Active and purposive vision　446
9.2　Geometry for 3D Vision　448
9.2.1　Basics of projective geometry　448
9.2.2　The single perspective camera　449
9.2.3　An overview of single camera calibration　453
9.2.4　Calibration of one camera from a known scene　455
9.2.5　Two cameras，stereopsis　457
9.2.6　The geometry of two cameras；the fundamental matrix　460
9.2.7　Relative motion of the camera；the essential matrix　462
9.2.8　Fundamental matrix estimation from image point correspondences　464
9.2.9　Applications of epipolar geometry in vision　466
9.2.10　Three and more cameras　471
9.2.11　Stereo correspondence algorithms　476
9.2.12　Active acquisition of range images　483
9.3　Radiometry and 3D vision　486
9.3.1　Radiometric considerations in determining gray-level　486
9.3.2　Surface reflectance　490
9.3.3　Shape from shading　494
9.3.4　Photometric stereo　498
9.4　Summary　499
9.5　Exercises　501
9.6　References　502
10　Use of 3D vision　508
10.1　Shape from X　508
10.1.1　Shape from motion　508
10.1.2　Shape from texture　515
10.1.3　Other shape from X techniques　517
10.2　Full 3D objects　519
10.2.1　3D objects，models，and related issues　519
10.2.2　Line labeling　521
10.2.3　Volumetric representation，direct measurements　523
10.2.4　Volumetric modeling strategies　525
10.2.5　Surface modeling strategies　527
10.2.6　Registering surface patches and their fusion to get a full 3D model　529
10.3　3D model-based vision　535
10.3.1　General considerations　535
10.3.2　Goad's algorithm　537
10.3.3　Model-based recognition of curved objects from intensity images　541
10.3.4　Model-based recognition based on range images　543
10.4　2D view-based representations of a 3D scene　544
10.4.1　Viewing space　544
10.4.2　Multi-view representations and aspect graphs　544
10.4.3　Geons as a 2D view-based structural representation　545
10.4.4　Visualizing 3D real-world scenes using stored collections of 2D views　546
10.5　Summary　551
10.6　Exercises　552
10.7　References　553
11　Mathematical morphology　559
11.1　Basic morphological concepts　559
11.2　Four morphological principles　561
11.3　Binary dilation and erosion　563
11.3.1　Dilation　563
11.3.2　Erosion　565
11.3.3　Hit-or-miss transformation　568
11.3.4　Opening and closing　568
11.4　Gray-scale dilation and erosion　568
11.4.1　Top surface，umbra，and gray-scale dilation and erosion　570
11.4.2　Umbra homeomorphism theorem，properties of erosion and dilation，opening and closing　573
11.4.3　Top hat transformation　574
11.5　Skeletons and object marking　576
11.5.1　Homotopic transformations　576
11.5.2　Skeleton，maximal ball　576
11.5.3　Thinning，thickening，and homotopic skeleton　578
11.5.4　Quench function，ultimate erosion　581
11.5.5　Ultimate erosion and distance functions　584
11.5.6　Geodesic transformations　585
11.5.7　Morphological reconstruction　586
11.6　Granulometry　589
11.7　Morphological segmentation and watersheds　590
11.7.1　Particles segmentation，marking，and watersheds　590
11.7.2　Binary morphological segmentation　592
11.7.3　Gray-scale segmentation，watersheds　594
11.8　Summary　595
11.9　Exercises　597
11.10　References　598
12　Linear discrete image transforms　600
12.1　Basic theory　600
12.2　Fourier transform　602
12.3　Hadamard transform　604
12.4　Discrete cosine transform　605
12.5　Wavelets　606
12.6　Other orthogonal image transforms　608
12.7　Applications of discrete image transforms　609
12.8　Summary　613
12.9　Exercises　617
12.10　References　619
13　Image data compression　621
13.1　Image data properties　622
13.2　Discrete image transforms in image data compression　623
13.3　Predictive compression methods　624
13.4　Vector quantization　629
13.5　Hierarchical and progressive compression methods　630
13.6　Comparison of compression methods　631
13.7　Other techniques　632
13.8　Coding　633
13.9　JPEG and MPEG image compression　634
13.9.1　JPEG—still image compression　634
13.9.2　MPEG-full-motion video compression　636
13.10　Summary　637
13.11　Exercises　640
13.12　References　641
14　Texture　646
14.1　Statistical texture description　649
14.1.1　Methods based on spatial frequencies　649
14.1.2　Co-occurrence matrices　651
14.1.3　Edge frequency　653
14.1.4　Primitive length(run length)　655
14.1.5　Laws' texture energy measures　565
14.1.6　Fractal texture description　657
14.1.7　Other statistical methods of texture description　659
14.2　Syntactic texture description methods　660
14.2.1　Shape chain grammars　661
14.2.2　Graph grammars　663
14.2.3　Primitive grouping in hierarchical textures　664
14.3　Hybrid texture description methods　666
14.4　Texture recognition method applications　667
14.5　Summary　668
14.6　Exercises　670
14.7　References　672
15　Motion analysis　679
15.1　Differential motion analysis methods　682
15.2　Optical flow　685
15.2.1　Optical flow computation　686
15.2.2　Global and local optical flow estimation　689
15.2.3　Optical flow computation approaches　690
15.2.4　Optical flow in motion analysis　693
15.3　Analysis based on correspondence of interest points　696
15.3.1　Detection of interest points　696
15.3.2　Correspondence of interest points　697
15.3.3　Object tracking　700
15.4　Kalman filters　708
15.4.1　Example　709
15.5　Summary　710
15.6　Exercises　712
15.7　References　714
16　Case studies　722
16.1　An optical music recognition system　722
16.2　Automated image analysis in cardiology　727
16.2.1　Robust analysis of coronary angiograms　730
16.2.2　Knowledge-based analysis of intra-vascular ultrasound　733
16.3　Automated identification of airway trees　738
16.4　Passive surveillance　744
16.5　References　750
Index　755