DIGITAL IMAGE PROCESSING USING MATLABPDF电子书下载

1 Introduction 1

Preview 1

1.1 Background 1

1.2 What Is Digital Image Processing？ 2

1.3 Background on MATLAB and the Image Processing Toolbox 4

1.4 Areas of Image Processing Covered in the Book 5

1.5 The Book Web Site 6

1.6 Notation 7

1.7 The MATLAB Working Environment 7

1.7.1 The MATLAB Desktop 7

1.7.2 Using the MATLAB Editor to Create M-Files 9

1.7.3 Getting Help 9

1.7.4 Saving and Retrieving a Work Session 10

1.8 How References Are Organized in the Book 11

Summary 11

2 Fundamentals 12

Preview 12

2.1 Digital Image Representation 12

2.1.1 Coordinate Conventions 13

2.1.2 Images as Matrices 14

2.2 Reading Images 14

2.3 Displaying Images 16

2.4 Writing Images 18

2.5 Data Classes 23

2.6 Image Types 24

2.6.1 Intensity Images 24

2.6.2 Binary Images 25

2.6.3 A Note on Terminology 25

2.7 Converting between Data Classes and Image Types 25

2.7.1 Converting between Data Classes 25

2.7.2 Converting between Image Classes and Types 26

2.8 Array Indexing 30

2.8.1 Vector Indexing 30

2.8.2 Matrix Indexing 32

2.8.3 Selecting Array Dimensions 37

2.9 Some Important Standard Arrays 37

2.10 Introduction to M-Function Programming 38

2.10.1 M-Files 38

2.10.2 Operators 40

2.10.3 Flow Control 49

2.10.4 Code Optimization 55

2.10.5 Interactive I/O 59

2.10.6 A Brief Introduction to Cell Arrays and Structures 62

Summary 64

3 Intensity Transformations and Spatial Filtering 65

Preview 65

3.1 Background 65

3.2 Intensity Transformation Functions 66

3.2.1 Function imadjust 66

3.2.2 Logarithmic and Contrast-Stretching Transformations 68

3.2.3 Some Utility M-Functions for Intensity Transformations 70

3.3 Histogram Processing and Function Plotting 76

3.3.1 Generating and Plotting Image Histograms 76

3.3.2 Histogram Equalization 81

3.3.3 Histogram Matching （Specification） 84

3.4 Spatial Filtering 89

3.4.1 Linear Spatial Filtering 89

3.4.2 Nonlinear Spatial Filtering 96

3.5 Image Processing Toolbox Standard Spatial Filters 99

3.5.1 Linear Spatial Filters 99

3.5.2 Nonlinear Spatial Filters 104

Summary 107

4 Frequency Domain Processing 108

Preview 108

4.1 The 2-D Discrete Fourier Transform 108

4.2 Computing and Visualizing the 2-D DFT in MATLAB 112

4.3 Filtering in the Frequency Domain 115

4.3.1 Fundamental Concepts 115

4.3.2 Basic Steps in DFT Filtering 121

4.3.3 An M-function for Filtering in the Frequency Domain 122

4.4 Obtaining Frequency Domain Filters from Spatial Filters 122

4.5 Generating Filters Directly in the Frequency Domain 127

4.5.1 Creating Meshgrid Arrays for Use in Implementing Filters in the Frequency Domain 128

4.5.2 Lowpass Frequency Domain Filters 129

4.5.3 Wireframe and Surface Plotting 132

4.6 Sharpening Frequency Domain Filters 136

4.6.1 Basic Highpass Filtering 136

4.6.2 High-Frequency Emphasis Filtering 138

Summary 140

5 Image Restoration 141

Preview 141

5.1 A Model of the Image Degradation/Restoration Process 142

5.2 Noise Models 143

5.2.1 Adding Noise with Function imnoise 143

5.2.2 Generating Spatial Random Noise with a Specified Distribution 144

5.2.3 Periodic Noise 150

5.2.4 Estimating Noise Parameters 153

5.3 Restoration in the Presence of Noise Only—Spatial Filtering 158

5.3.1 Spatial Noise Filters 159

5.3.2 Adaptive Spatial Filters 164

5.4 Periodic Noise Reduction by Frequency Domain Filtering 166

5.5 Modeling the Degradation Function 166

5.6 Direct Inverse Filtering 169

5.7 Wiener Filtering 170

5.8 Constrained Least Squares （Regularized） Filtering 173

5.9 Iterative Nonlinear Restoration Using the Lucy-Richardson Algorithm 176

5.10 Blind Deconvolution 179

5.11 Geometric Transformations and Image Registration 182

5.11.1 Geometric Spatial Transformations 182

5.11.2 Applying Spatial Transformations to Images 187

5.11.3 Image Registration 191

Summary 193

6 Color Image Processing 194

Preview 194

6.1 Color Image Representation in MATLAB 194

6.1.1 RGB Images 194

6.1.2 Indexed Images 197

6.1.3 IPT Functions for Manipulating RGB and Indexed Images 199

6.2 Converting to Other Color Spaces 204

6.2.1 NTSC Color Space 204

6.2.2 The YCbCr Color Space 205

6.2.3 The HSV Color Space 205

6.2.4 The CMY and CMYK Color Spaces 206

6.2.5 The HSI Color Space 207

6.3 The Basics of Color Image Processing 215

6.4 Color Transformations 216

6.5 Spatial Filtering of Color Images 227

6.5.1 Color Image Smoothing 227

6.5.2 Color Image Sharpening 230

6.6 Working Directly in RGB Vector Space 231

6.6.1 Color Edge Detection Using the Gradient 232

6.6.2 Image Segmentation in RGB Vector Space 237

Summary 241

7 Wavelets 242

Preview 242

7.1 Background 242

7.2 The Fast Wavelet Transform 245

7.2.1 FWTs Using the Wavelet Toolbox 246

7.2.2 FWTs without the Wavelet Toolbox 252

7.3 Working with Wavelet Decomposition Structures 259

7.3.1 Editing Wavelet Decomposition Coefficients without the Wavelet Toolbox 262

7.3.2 Displaying Wavelet Decomposition Coefficients 266

7.4 The Inverse Fast Wavelet Transform 271

7.5 Wavelets in Image Processing 276

Summary 281

8 Image Compression 282

Preview 282

8.1 Background 283

8.2 Coding Redundancy 286

8.2.1 Huffman Codes 289

8.2.2 Huffman Encoding 295

8.2.3 Huffman Decoding 301

8.3 Interpixel Redundancy 309

8.4 Psychovisual Redundancy 315

8.5 JPEG Compression 317

8.5.1 JPEG 318

8.5.2 JPEG 2000 325

Summary 333

9 Morphological Image Processing 334

Preview 334

9.1 Preliminaries 335

9.1.1 Some Basic Concepts from Set Theory 335

9.1.2 Binary Images， Sets， and Logical Operators 337

9.2 Dilation and Erosion 337

9.2.1 Dilation 338

9.2.2 Structuring Element Decomposition 341

9.2.3 The strel Function 341

9.2.4 Erosion 345

9.3 Combining Dilation and Erosion 347

9.3.1 Opening and Closing 347

9.3.2 The Hit-or-Miss Transformation 350

9.3.3 Using Lookup Tables 353

9.3.4 Function bwmorph 356

9.4 Labeling Connected Components 359

9.5 Morphological Reconstruction 362

9.5.1 Opening by Reconstruction 363

9.5.2 Filling Holes 365

9.5.3 Clearing Border Objects 366

9.6 Gray-Scale Morphology 366

9.6.1 Dilation and Erosion 366

9.6.2 Opening and Closing 369

9.6.3 Reconstruction 374

Summary 377

10 Image Segmentation 378

Preview 378

10.1 Point， Line， and Edge Detection 379

10.1.1 Point Detection 379

10.1.2 Line Detection 381

10.1.3 Edge Detection Using Function edge 384

10.2 Line Detection Using the Hough Transform 393

10.2.1 Hough Transform Peak Detection 399

10.2.2 Hough Transform Line Detection and Linking 401

10.3 Thresholding 404

10.3.1 Global Thresholding 405

10.3.2 Local Thresholding 407

10.4 Region-Based Segmentation 407

10.4.1 Basic Formulation 407

10.4.2 Region Growing 408

10.4.3 Region Splitting and Merging 412

10.5 Segmentation Using the Watershed Transform 417

10.5.1 Watershed Segmentation Using the Distance Transform 418

10.5.2 Watershed Segmentation Using Gradients 420

10.5.3 Marker-Controlled Watershed Segmentation 422

Summary 425

11 Representation and Description 426

Preview 426

11.1 Background 426

11.1.1 Cell Arrays and Structures 427

11.1.2 Some Additional MATLAB and IPT Functions Used in This Chapter 432

11.1.3 Some Basic Utility M-Functions 433

11.2 Representation 436

11.2.1 Chain Codes 436

11.2.2 Polygonal Approximations Using Minimum-Perimeter Polygons 439

11.2.3 Signatures 449

11.2.4 Boundary Segments 452

11.2.5 Skeletons 453

11.3 Boundary Descriptors 455

11.3.1 Some Simple Descriptors 455

11.3.2 Shape Numbers 456

11.3.3 Fourier Descriptors 458

11.3.4 Statistical Moments 462

11.4 Regional Descriptors 463

11.4.1 Function regionprops 463

11.4.2 Texture 464

11.4.3 Moment Invariants 470

11.5 Using Principal Components for Description 474

Summary 483

12 Object Recognition 484

Preview 484

12.1 Background 484

12.2 Computing Distance Measures in MATLAB 485

12.3 Recognition Based on Decision-Theoretic Methods 488

12.3.1 Forming Pattern Vectors 488

12.3.2 Pattern Matching Using Minimum-Distance Classifiers 489

12.3.3 Matching by Correlation 490

12.3.4 Optimum Statistical Classifiers 492

12.3.5 Adaptive Learning Systems 498

12.4 Structural Recognition 498

12.4.1 Working with Strings in MATLAB 499

12.4.2 String Matching 508

Summary 513

Appendix A Function Summary 514

Appendix B ICE and MATLAB Graphical User Interfaces 527

Appendix C M-Functions 552

Bibliography 594

Index 597