Practical Algorithms for Image Analysis with CD-ROM > 영상처리

1 Introduction 1

1.1 Introduction 1

1.2 Annotated Section Overview 5

1.3 A Guided Tour 14

2 Global Image Analysis 23

2.1 Intensity Histogram: Global Features 25

2.2 Histogram Transformations: Global Enhancement 29

2.3 Combining Images 39

2.4 Geometric Image Transformations 45

2.5 Color Image Transformations 53

3 Gray-Scale Image Analysis 58

3.1 Local Image Operations: Convolution 61

3.2 Noise Reduction 70

3.3 Edge Enhancement and Flat Fielding 77

3.4 Edge and Peak Point Detection 82

3.5 Advanced Edge Detection 92

3.6 Subsampling 99

3.7 Multiresolution Analysis 103

3.8 Template Matching 108

3.9 Gabor Wavelet Analysis 112

3.10 Binarization 118

4 Binary Image Analysis 128

4.1 Morphological and Cellular Processing 131

4.2 Binary Noise Removal 139

4.3 Region Detection 142

4.4 Shape Analysis: Geometrical Features and Moments 154

4.5 Advanced Shape Analysis: Fourier Descriptors 162

4.6 Convex Hull of Polygons 166

4.7 Thinning 170

4.8 Linewidth Determination 174

4.9 Global Features and Image Profiles 177

4.10 Hough Transform 182

5 Analysis of Lines and Line Patterns 186

5.1 Chain Coding 188

5.2 Line Features and Noise Reduction 194

5.3 Polygonalization 201

5.4 Critical Point Detection 207

5.5 Straight-Line Fitting 211

5.6 Cubic Spline Fitting 216

5.7 Morphology and Topology of Line Patterns 220

6 Analysis of Point Patterns 231

6.1 The Voronoi Diagram of Point Patterns 232

6.2 Spatial Statistics of Point Patterns: Distribution Functions 238

6.3 Topology and Geometry of Cellular Patterns 245

6.4 The k-Nearest-Neighbor (k-NN) Problem 251

7 Frequency Domain Analysis 256

7.1 The 2DDiscrete Fourier Transform 258

7.2 Frequency Domain Filtering 275

8 Program Descriptions 283

8.1 Introduction 283

8.2 Programs by Chapter 283

8.3 Alphabetical Listing of Programs 286

9 Projects 316

Appendix Synopsis of Important Concepts 325

A.1 The Fourier Transform: Spatial Domain Versus Frequency Domain 326

A.2 Linear Systems: Impulse Response, Convolution, and Transfer Function 330

A.3 Special-Purpose Filters 335

A.4 The Whittaker-Shannon Sampling Theorem 337

A.5 Commonly Used Data Structures 340

Index 345