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1 Introduction1

Preview1

1.1 Background1

1.2 What Is Digital Image Processing？2

1.3 Background on MATLAB and the Image Processing Toolbox4

1.4 Areas of Image Processing Covered in the Book5

1.5 The Book Web Site6

1.6 Notation7

1.7 The MATLAB Working Environment7

1.7.1 The MATLAB Desktop7

1.7.2 Using the MATLAB Editor to Create M-Files9

1.7.3 Getting Help9

1.7.4 Saving and Retrieving a Work Session10

1.8 How References Are Organized in the Book11

Summary11

2 Fundamentals12

Preview12

2.1 Digital Image Representation12

2.1.1 Coordinate Conventions13

2.1.2 Images as Matrices14

2.2 Reading Images14

2.3 Displaying Images16

2.4 Writing Images18

2.5 Data Classes23

2.6 Image Types24

2.6.1 Intensity Images24

2.6.2 Binary Images25

2.6.3 A Note on Terminology25

2.7 Converting between Data Classes and Image Types25

2.7.1 Converting between Data Classes25

2.7.2 Converting between Image Classes and Types26

2.8 Array Indexing30

2.8.1 Vector Indexing30

2.8.2 Matrix Indexing32

2.8.3 Selecting Array Dimensions37

2.9 Some Important Standard Arrays37

2.10 Introduction to M-Function Programming38

2.10.1 M-Files38

2.10.2 Operators40

2.10.3 Flow Control49

2.10.4 Code Optimization55

2.10.5 Interactive I/O59

2.10.6 A Brief Introduction to Cell Arrays and Structures62

Summary64

3 Intensity Transformations and Spatial Filtering65

Preview65

3.1 Background65

3.2 Intensity Transformation Functions66

3.2.1 Function imadjust66

3.2.2 Logarithmic and Contrast-Stretching Transformations68

3.2.3 Some Utility M-Functions for Intensity Transformations70

3.3 Histogram Processing and Function Plotting76

3.3.1 Generating and Plotting Image Histograms76

3.3.2 Histogram Equalization81

3.3.3 Histogram Matching （Specification）84

3.4 Spatial Filtering89

3.4.1 Linear Spatial Filtering89

3.4.2 Nonlinear Spatial Filtering96

3.5 Image Processing Toolbox Standard Spatial Filters99

3.5.1 Linear Spatial Filters99

3.5.2 Nonlinear Spatial Filters104

Summary107

4 Frequency Domain Processing108

Preview108

4.1 The 2-D Discrete Fourier Transform108

4.2 Computing and Visualizing the 2-D DFT in MATLAB112

4.3 Filtering in the Frequency Domain115

4.3.1 Fundamental Concepts115

4.3.2 Basic Steps in DFT Filtering121

4.3.3 An M-function for Filtering in the Frequency Domain122

4.4 Obtaining Frequency Domain Filters from Spatial Filters122

4.5 Generating Filters Directly in the Frequency Domain127

4.5.1 Creating Meshgrid Arrays for Use in Implementing Filters in the Frequency Domain128

4.5.2 Lowpass Frequency Domain Filters129

4.5.3 Wireframe and Surface Plotting132

4.6 Sharpening Frequency Domain Filters136

4.6.1 Basic Highpass Filtering136

4.6.2 High-Frequency Emphasis Filtering138

Summary140

5 Image Restoration141

Preview141

5.1 A Model of the Image Degradation/Restoration Process142

5.2 Noise Models143

5.2.1 Adding Noise with Function imnoise143

5.2.2 Generating Spatial Random Noise with a Specified Distribution144

5.2.3 Periodic Noise150

5.2.4 Estimating Noise Parameters153

5.3 Restoration in the Presence of Noise Only—Spatial Filtering158

5.3.1 Spatial Noise Filters159

5.3.2 Adaptive Spatial Filters164

5.4 Periodic Noise Reduction by Frequency Domain Filtering166

5.5 Modeling the Degradation Function166

5.6 Direct Inverse Filtering169

5.7 Wiener Filtering170

5.8 Constrained Least Squares （Regularized） Filtering173

5.9 Iterative Nonlinear Restoration Using the Lucy-Richardson Algorithm176

5.10 Blind Deconvolution179

5.11 Geometric Transformations and Image Registration182

5.11.1 Geometric Spatial Transformations182

5.11.2 Applying Spatial Transformations to Images187

5.11.3 Image Registration191

Summary193

6 Color Image Processing194

Preview194

6.1 Color Image Representation in MATLAB194

6.1.1 RGB Images194

6.1.2 Indexed Images197

6.1.3 IPT Functions for Manipulating RGB and Indexed Images199

6.2 Converting to Other Color Spaces204

6.2.1 NTSC Color Space204

6.2.2 The YCbCr Color Space205

6.2.3 The HSV Color Space205

6.2.4 The CMY and CMYK Color Spaces206

6.2.5 The HSI Color Space207

6.3 The Basics of Color Image Processing215

6.4 Color Transformations216

6.5 Spatial Filtering of Color Images227

6.5.1 Color Image Smoothing227

6.5.2 Color Image Sharpening230

6.6 Working Directly in RGB Vector Space231

6.6.1 Color Edge Detection Using the Gradient232

6.6.2 Image Segmentation in RGB Vector Space237

Summary241

7 Wavelets242

Preview242

7.1 Background242

7.2 The Fast Wavelet Transform245

7.2.1 FWTs Using the Wavelet Toolbox246

7.2.2 FWTs without the Wavelet Toolbox252

7.3 Working with Wavelet Decomposition Structures259

7.3.1 Editing Wavelet Decomposition Coefficients without the Wavelet Toolbox262

7.3.2 Displaying Wavelet Decomposition Coefficients266

7.4 The Inverse Fast Wavelet Transform271

7.5 Wavelets in Image Processing276

Summary281

8 Image Compression282

Preview282

8.1 Background283

8.2 Coding Redundancy286

8.2.1 Huffman Codes289

8.2.2 Huffman Encoding295

8.2.3 Huffman Decoding301

8.3 Interpixel Redundancy309

8.4 Psychovisual Redundancy315

8.5 JPEG Compression317

8.5.1 JPEG318

8.5.2 JPEG 2000325

Summary333

9 Morphological Image Processing334

Preview334

9.1 Preliminaries335

9.1.1 Some Basic Concepts from Set Theory335

9.1.2 Binary Images， Sets， and Logical Operators337

9.2 Dilation and Erosion337

9.2.1 Dilation338

9.2.2 Structuring Element Decomposition341

9.2.3 The strel Function341

9.2.4 Erosion345

9.3 Combining Dilation and Erosion347

9.3.1 Opening and Closing347

9.3.2 The Hit-or-Miss Transformation350

9.3.3 Using Lookup Tables353

9.3.4 Function bwmorph356

9.4 Labeling Connected Components359

9.5 Morphological Reconstruction362

9.5.1 Opening by Reconstruction363

9.5.2 Filling Holes365

9.5.3 Clearing Border Objects366

9.6 Gray-Scale Morphology366

9.6.1 Dilation and Erosion366

9.6.2 Opening and Closing369

9.6.3 Reconstruction374

Summary377

10 Image Segmentation378

Preview378

10.1 Point， Line， and Edge Detection379

10.1.1 Point Detection379

10.1.2 Line Detection381

10.1.3 Edge Detection Using Function edge384

10.2 Line Detection Using the Hough Transform393

10.2.1 Hough Transform Peak Detection399

10.2.2 Hough Transform Line Detection and Linking401

10.3 Thresholding404

10.3.1 Global Thresholding405

10.3.2 Local Thresholding407

10.4 Region-Based Segmentation407

10.4.1 Basic Formulation407

10.4.2 Region Growing408

10.4.3 Region Splitting and Merging412

10.5 Segmentation Using the Watershed Transform417

10.5.1 Watershed Segmentation Using the Distance Transform418

10.5.2 Watershed Segmentation Using Gradients420

10.5.3 Marker-Controlled Watershed Segmentation422

Summary425

11 Representation and Description426

Preview426

11.1 Background426

11.1.1 Cell Arrays and Structures427

11.1.2 Some Additional MATLAB and IPT Functions Used in This Chapter432

11.1.3 Some Basic Utility M-Functions433

11.2 Representation436

11.2.1 Chain Codes436

11.2.2 Polygonal Approximations Using Minimum-Perimeter Polygons439

11.2.3 Signatures449

11.2.4 Boundary Segments452

11.2.5 Skeletons453

11.3 Boundary Descriptors455

11.3.1 Some Simple Descriptors455

11.3.2 Shape Numbers456

11.3.3 Fourier Descriptors458

11.3.4 Statistical Moments462

11.4 Regional Descriptors463

11.4.1 Function regionprops463

11.4.2 Texture464

11.4.3 Moment Invariants470

11.5 Using Principal Components for Description474

Summary483

12 Object Recognition484

Preview484

12.1 Background484

12.2 Computing Distance Measures in MATLAB485

12.3 Recognition Based on Decision-Theoretic Methods488

12.3.1 Forming Pattern Vectors488

12.3.2 Pattern Matching Using Minimum-Distance Classifiers489

12.3.3 Matching by Correlation490

12.3.4 Optimum Statistical Classifiers492

12.3.5 Adaptive Learning Systems498

12.4 Structural Recognition498

12.4.1 Working with Strings in MATLAB499

12.4.2 String Matching508

Summary513

Appendix A Function Summary514

Appendix B ICE and MATLAB Graphical User Interfaces527

Appendix C M-Functions552

Bibliography594

Index597