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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
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