Preface	5
	Contents	9
	Part I Foundation	14
	1 Applications and Tools	16
	1.1 A Tool for Science and Technique	16
	1.2 Examples of Applications	17
	1.3 Hierarchy of Image Processing Operations	28
	1.4 Image Processing and Computer Graphics	30
	1.5 Cross-disciplinary Nature of Image Processing	30
	1.6 Human and Computer Vision	31
	1.7 Components of an Image Processing System	34
	1.8 Exercises	39
	1.9 Further Readings	41
	2 Image Representation	44
	2.1 Introduction	44
	2.2 Spatial Representation of Digital Images	44
	2.3 Wave Number Space and Fourier Transform	54
	2.4 Discrete Unitary Transforms	76
	2.5 Fast Algorithms for Unitary Transforms	80
	2.6 Exercises	90
	2.7 Further Readings	93
	3 Random Variables and Fields	94
	3.1 Introduction	94
	3.2 Random Variables	96
	3.3 Multiple Random Variables	100
	3.4 Probability Density Functions	104
	3.5 Stochastic Processes and Random Fields	111
	3.6 Exercises	115
	3.7 Further Readings	117
	4 Neighborhood Operations	118
	4.1 Basic Properties and Purpose	118
	4.2 Linear Shift-Invariant Filters	121
	4.3 Rank Value Filters	132
	4.4 LSI-Filters: Further Properties	133
	4.5 Recursive Filters	135
	4.6 Recursive Filters	144
	4.7 Further Readings	147
	5 Multiscale Representation	148
	5.1 Scale	148
	5.2 Multigrid Representations	151
	5.3 Scale Spaces	157
	5.4 Exercises	165
	5.5 Further Readings	166
	Part II Image Formation and Preprocessing	168
	6 Quantitative Visualization	170
	6.1 Introduction	170
	6.2 Radiometry, Photometry, Spectroscopy, and Color	172
	6.3 Waves and Particles	181
	6.4 Interactions of Radiation with Matter	187
	6.5 Exercises	199
	6.6 Further Readings	200
	7 Image Formation	202
	7.1 Introduction	202
	7.2 World and Camera Coordinates	202
	7.3 Ideal Imaging: Perspective Projection	205
	7.4 Real Imaging	208
	7.5 Radiometry of Imaging	214
	7.6 Linear System Theory of Imaging	218
	7.7 Homogeneous Coordinates	225
	7.8 Exercises	227
	7.9 Further Readings	228
	8 3-D Imaging	230
	8.1 Basics	230
	8.2 Depth from Triangulation	234
	8.3 Depth from Time-of-Flight	241
	8.4 Depth from Phase: Interferometry	242
	8.5 Shape from Shading	242
	8.6 Depth from Multiple Projections: Tomography	248
	8.7 Exercises	254
	8.8 Further Readings	255
	9 Digitization, Sampling, Quantization	256
	9.1 Definition and E.ects of Digitization	256
	9.2 Image Formation, Sampling, Windowing	258
	9.3 Reconstruction from Samples	262
	9.4 Multidimensional Sampling on Nonorthogonal Grits	264
	9.5 Quantization	266
	9.6 Exercises	267
	9.7 Further Readings	268
	10 Pixel Processing	270
	10.1 Introduction	270
	10.2 Homogeneous Point Operations	271
	10.3 Inhomogeneous Point Operations	281
	10.4 Geometric Transformations	288
	10.5 Interpolation	292
	10.6 Optimized Interpolation	299
	10.7 Multichannel Point Operations	304
	10.8 Exercises	306
	10.9 Further Redings	308
	Part III Feature Extraction	310
	11 Averaging	312
	11.1 Introduction	312
	11.2 General Properties of Averaging Filters	312
	11.3 Box Filter	315
	11.4 Binomial Filter	319
	11.5 Effcient Large-Scale Averaging	325
	11.6 Nonlinear Averaging	334
	11.7 Averaging in Multichannel Images	339
	11.8 Exercises	341
	11.9 Further Redings	343
	12 Edges	344
	12.1 Introduction	344
	12.2 Differential Description of Signal Changes	345
	12.3 General Properties of Edge Filters	348
	12.4 Gradient-Based Edge Detection	351
	12.5 Edge Detection by Zero Crossings	358
	12.6 Optimized Edge Detection	360
	12.7 Regularized Edge Detection	362
	12.8 Edges in Multichannel Images	366
	12.9 Exercises	368
	12.10 Further Redings	370
	13 Simple Neighborhoods	372
	13.1 Introduction	372
	13.2 Properties of Simple Neighborhoods	373
	13.3 First-Order Tensor Representation	377
	13.4 Local Wave Number and Phase	388
	13.5 Further Tensor Representations	397
	13.6 Exercises	408
	13.7 Further Redings	409
	14 Motion	410
	14.1 Introduction	410
	14.2 Basics	411
	14.3 First-Order Di.erential Methods	426
	14.4 Tensor Methods	431
	14.5 Correlation Methods	436
	14.6 Phase Method	439
	14.7 Additional Methods	441
	14.8 Exercises	447
	14.9 Fruther Readings	447
	15 Texture	448
	15.1 Introduction	448
	15.2 First-Order Statistics	451
	15.3 Rotation and Scale Variant Texture Features	455
	15.4 Exercises	459
	15.5 Further Readings	459
	Part IV Image Analysis	460
	16 Segmentation	462
	16.1 Introduction	462
	16.2 Pixel-Based Segmentation	462
	16.3 Edge-Based Segmentation	466
	16.4 Region-Based Segmentation	467
	16.5 Model-Based Segmentation	471
	16.6 Exercises	474
	16.7 Further Readings	475
	17 Regularization and Modeling	476
	17.1 Introduction	476
	17.2 Continuous Modeling I: Veriational Approach	479
	17.3 Continuous Modeling II: Diffusion	486
	17.4 Discrete Modeling: Inverse Problems	491
	17.5 Inverse Filtering	499
	17.6 Further Equivalent Approaches	505
	17.7 Exercises	511
	17.8 Further Redings	513
	18 Morphology	514
	18.1 Introduction	514
	18.2 Neighborhood Operations on Binary Images	514
	18.3 General Properties	516
	18.4 Composite Morphological Operators	519
	18.5 Exercises	525
	18.6 Furtheer Readings	527
	19 Shape Presentation and Analysis	528
	19.1 Introduction	528
	19.2 Representation of Shape	528
	19.3 Moment-Based Shape Features	533
	19.4 Fourier Descriptors	535
	19.5 Shape Parameters	541
	19.6 Exercises	544
	19.7 Further Readings	545
	20 Classification	546
	20.1 Introduction	546
	20.2 Feature Space	549
	20.3 Simple Classi.cation Techniques	556
	20.4 Exercises	561
	20.5 Further Readings	562
	Part V Reference Part	564
	A Reference Material	566
	B Notation	590
	Bibliography	598
	Index	610