Chapter 1 MATLAB image processing tools and installation settings 2

1. 1 MATLAB 2nd Edition

New functions of image processing toolbox version 1.2 7. 1.3

1.2. 1 uses a new corner function to detect corners in an image.

1.2.2 Effectively display and navigate oversized images in any format in IMTOOL 4.

1.2.3 Use the blockproc function to control the filling action 5.

1.2.4 blockproc function supports writing 5 in JPEG2000 file format.

1.2.5 dicomread function enhancement 5

The image enlargement area of the 1.2.6 nitfinfo function now returns a value of 6.

1.2.7 Function for improving the performance of the new version 6

1.2.8 Deleted function and function element 6.

New functions of image acquisition toolbox version 1.3 4.0 6

GigE Vision 7 supports 1.3. 1.

1.3.2 supports Linux operating system 7.

1.3.3 spreading code generation of signal acquisition module of video equipment 8

1.3.4 supports apple OS 8.

1.3.5 supports the new Matrox hardware8.

1.3.6 Personal Evaluation of New Image Related Toolbox 8

1.4 MATLAB installation problem 9

1.4. 1 Matlab and hardware conflict 9

1.4.2 Chinese Directory 10 Problems caused by MATLAB installation

1.4.3 license conflict 1 1

1.4.4 Compatibility of Matlab with Windows Vista Operating System

1.4.5 MATLAB11Other problems related to installation.

1.5 MATLAB R20 10b Installation Instructions 12

1.6 summary 16

The second chapter is the characteristics and learning methods of MATLAB image processing 17.

2.1Comparison between MATLAB and other image processing software 17

2.2 development characteristics of MATLAB image processing program 19

2.3 MATLAB image processing applicable personnel 19

2.4 learning MATLAB 2 1

2.4. the connection between1visual image and MATLAB 2 1

2.4.2 image processing in MATLAB

2.4.3 3D data field processing in matlab23

2.5 Basic Problems of Matlab Image Processing Research 24

2.6 Correlation function of new function of MATLAB image processing 25

2.6. 1 function command angle 25

Function command rsetwrite 27

Function command blockproc 29

2.7 Updated Demo 32

2.7. 1 Block processing of large images 33

2.7.2 Calculating statistical data of large images 36

2.7.3 Batch processing multiple image files in parallel 4 1

2.7.4 Introduction of New Demos in Video and Image Processing Module Library 45

2.8 Summary 46

Chapter 3 Digital Image Basis 47

3. 1 digital image 47

3. 1. 1 the concept of image 47

3. 1.2 Importance of image information 49

3.2 Basic knowledge of image acquisition 50

Television camera 50

The charge couple device 50

Resolution 5 1

3.2.4 Basic knowledge of image acquisition card 52

3.3 Image Processing and Analysis 52

3.3. 1 image processing and image analysis

Image conversion 55

3.4 Application of Digital Image Technology 56

3.4. 1 Computer image analysis and processing in material science research 56

3.4.2 MATLAB material science related processing 57

3.4.3 Medical Images 62

3.4.4 Correlation processing of MATLAB medical images 64

3.4.5 Using MATLAB to find the earthquake center 67

3.4.6 Common digital imaging industry applications 7 1

3.5 MATLAB readable image and video format 72

3.5. 1 image format 72

Video format 76

3.6 Summary 78

Chapter 4 MATLAB graphics drawing 79

4. 1 Drawing of basic 2D graphics 79

Create a simple 2D diagram 79

4. 1.2 Accurate Drawing 83

4. 1.3 2D graphic decoration 85

4. 1.4 graphic window 86, which supports various graphics * * *

4.2 Drawing of Special 2D Graphics 87

Histogram 87

Bar chart 88

4.2.3 Pie chart 90

4.2.4 Schematic diagram of handle 9 1

Step diagram 92

4.2.6 Area Diagram 93

Comet map 94

Pareto diagram 94

Scatter diagram 95

4.2. 10 scatter plot 96

4.2. 1 1 polar diagram 97

4.2. 12 contour map 98

4.3 Drawing of 3D graphics 99

Create a simple 3D graphic 99

4.3.2 Three-dimensional linear diagram 99

4.3.3 Generation of Plane Grid Points 100

4.3.4 Surface grid diagram and grid diagram 10 1

4.4 Application Example 102

4.4. 1 3D Drawing Program 102 Compilation Example

4.4.2 Excel calls MATLAB 3D drawing 103.

Cam Drawing 105

4.5 Summary 106

Chapter 5 Color and 3D Object Description 107

5. 1 color model classification 107

Mixed color means 5. 1. 1 color 108.

Color means 5. 1.2 color 109.

5.2 Related knowledge of color 109

5.2. 1 chromaticity related knowledge 109

5.2.2 CIE chromaticity diagram 1 12

5.3 Common color mode-color image mode 1 15

5.3. 1 RGB mode 1 15

5.3.2 CMYK mode 1 16

Laboratory mode 1 16

5.3.4 HSV mode 1 16

5.3.5 HSL mode 1 17

YUV mode 1 17

5.3.7 YCbCr mode 1 18

5.3.8 YIQ mode 1 18

5.4 MATLAB color space conversion 1 18

5.4. 1 YIQ space and RGB space conversion 1 18

5.4.2 Transformation between HSV space and RGB space 120

5.4.3 transformation between ycbcr space and RGB space 122

5.5 Common video color coding 124

YUV 124

YCbCr 124

5.6 3D Object Description 125

5.6. 1 2D Cartesian coordinate system 125

5.6.2 3D Cartesian Coordinate System 125

5.6.3 Draw a triangle 126

5.6.4 3D primitive 126

5.6.5 Normal vector of surface and vertex 127

5.7 Summary 128

Chapter VI Lighting and Materials of MATLAB 129

6. 1 OpenGL base 129

6. 1. 1 Basic understanding of OpenGL 129

6. 1.2 OpenGL workflow 130

6. 1.3 OpenGL graphic operation steps 13 1

6. 1.4 OpenGL basic function 13 1

6. 1.5 Basic concepts of realistic graphics 132

6. 1.6 lighting model 132

6. 1.7 shading 133

6. 1.8 material 134

6.2 MATLAB image rendering example 135

6.2. 1 formula generation data image rendering 135.

6.2.2 Different Rendering Effects of Sphere 136

6.3 light objects 137

Lighting instruction 137

6.3.2 Add lighting to the scene 138.

6.3.3 Attributes affecting lighting effect 138

6.3.4 Lighting Algorithm 139

6.4 Reflection characteristics of graphic objects-Material 140

6.4. 1 specular reflection and diffuse reflection 140

6.4.2 Environmental Lighting 140

6.4.3 mirror index 14 1

6.4.4 Mirror color reflection coefficient 14 1

6.4.5 Backlight 14 1

6.4.6 Lighting configuration of data space 142

6.5 Summary 143

Chapter VII Visualization of Scientific Computing 145

7. 1 Visualization Basis of Scientific Computing 145

7. 1.65438+

7. 1.2 Application field 146

7. 1.3 Application Example 149

7.2 Common Methods for Visualization of Scientific Computing 149

7.2./KOOC-0/2D Visualization Method of Plane Data Field/KOOC-0/49

7.2.2 3D data field method 150

7.2.3 Visualization method of vector field 152

7.3 Visualization of Plane Data Field 153

7.4 3D Flow Field Diagram 154

7.4. 1 flow cone-cone function 154

7.4.2 streamline diagram-streamline function 155

7.4.3 Flowchart-Flow Tube Function 156

7.4.4 Flow Zone Diagram-Flow Zone Function 157

7.4.5 Vector field with conic graph 159

7.5 Summary 16 1

In chapter 8, the image of MATLAB used for acoustic calculation is 162.

8. 1 sound field distribution 162

8. 1. 1 sound field of pulsating spherical sound source 163

8. 1.2 radiated sound field of two in-phase small ball sources 167

8. 1.3 radiation of circular piston on infinite barrier 174

8.2 directivity of acoustic emission array 186

8.2. 1 array directivity 186

Sensor array 189

8.3 Overview of this chapter 202

Part 2 Image Processing Toolbox Details 203

Chapter 9 Image Processing Toolbox Foundation 204

9. 1 Basic operations of image processing 204

9.2 Advanced Application of Image Processing 206

9.3 The basic image types supported by the image processing toolbox are 2 1 1.

9.3. 1 index color image 2 1 1

9.3.2 Gray image 2 12

9.3.3 RGB image 2 12

9.3.4 binary image 2 13

9.3.5 Multi-frame image 2 13

9.4 image type conversion 2 14

9.4. 1 dithering algorithm image conversion 2 14

9.4.2 RGB image is converted into gray image 2 15.

9.4.3 Convert RGB image into index image 2 16.

9.4.4 Gray image is converted into index image 2 17.

9.4.5 Index image is converted into gray image 2 18.

9.4.6 The index image is converted into RGB image 2 19.

9.4.7 Transform the image into a binary image by threshold method 2 19.

9.4.8 Transform the value-based grayscale image into an index image 220.

9.4.9 Matrix is converted into image 22 1.

9.5 Summary 22 1

Chapter 10 image transformation 222

10. 1 image conversion overview 222

10.2 Fourier transform 223

10.2. 1 one-dimensional continuous Fourier transform 223

10.2.2 one-dimensional discrete Fourier transform 223

10.2.3 2D continuous Fourier transform

10.2.4 2D discrete Fourier transform 224

10.2.5 Fast Fourier Transform Function in Matlab 226

10.3 Properties of Discrete Fourier Transform 228

10.3. 1 separability 228

10.3.2 translation 229

10.3.3 periodicity and ** yoke symmetry 23 1

10.3.4 Rotation Attribute 23 1

Linear attribute 23 1

The relationship between10.3.6f (0,0) and the average image value 232.

10.3.7 Fourier transform of the image processed by Laplacian operator 232

10.3.8 convolution and related theorem 232

Application of 10.4 Fast Fourier Transform (FFT)

10.4. 1 filter frequency response 233

10.4.2 Fast Convolution 233

10.4.3 image feature recognition 235

10.5 discrete cosine transform 236

Fourier transform of the continuous real even function of 10.5+0.50000.000000000505

10.5.2 Discrete Cosine Transform 237

Discrete cosine transform function MATLAB 238 in 10.6

10.6. 1 discrete 2D cosine transform 238

10.6.2 2D inverse discrete cosine transform 239

10.7 Discrete Cosine Transform and Image Compression 240

10.8 Ladon transform 24 1

10.8. 1 Radon transform of the image in the specified direction 2442

10.8.2 detecting straight line 243 with Ladon transform.

10.8.3 radon inverse transform and its application24444.000000000003

10.9 Summary 247

Chapter 1 1 Mathematical Morphology 248

1 1. 1 mathematical morphological basis 248

The concept of mathematical morphology:11.1.1.2654436

1 1. 1.2 the application of mathematical morphology .56666.666666660666

11.1.3 common mathematical morphological functions in MATLAB 250

1 1. 1.4 general application steps of mathematical morphology 250

1 1.2 Basic operations of mathematical morphology 252

1 1.2. 1 expansion and corrosion .50000.00000000015

1 1.2.2 Mathematical morphological reconstruction 258

1 1.2.3 distance transformation 259

1 1.2.4 object, area and feature metrics 26 1

1 1.2.5 lookup table 262

1 1.2.6 Digital Recognition Example Based on Mathematical Morphology 263

11.2.7 the application of mathematical morphology function of MATLAB in work566661

1 1.3 Summary 265

Chapter 12 image enhancement 266

12. 1 image enhancement overview 266

12. 1. 1 spatial transformation enhancement 266

12. 1.2 spatial filtering enhancement 267

12. 1.3 frequency domain enhancement 267

12.2 point operation 268

12.2. 1 gray level correction 268

12.2.2 gray scale transformation 269

12.2.3 histogram correction 270

12.3 MATLAB gray transformation 27 1

12.3. 1 imadjust function271

12.3.2 Dynamic range compression 274

12.4 MATLAB histogram correction 274

1 histogram equalization 275

12.4.2 histogram matching 276

12.5 smoothing filter 277

12.5. 1 mask denoising method 279

12.5.2 Neighborhood average method 28 1

12.5.3 Multi-graph average method 283

12.6 median filter 283

12.7 sharpening filter 285

1 spatial high-pass filter 286

12.7.2 Gradual Image Output Method 287

12.8 frequency domain enhancement 289

Butterworth Low Pass Filter Example 289

12.8.2 homomorphic filtering 29 1

12.9 Pseudo-color processing 293

Pseudo-color processing of 12.9. 1 color image30000.000000000505

12.9.2 Pseudo-color processing by gray level layering method 295

On the color processing of 12.9.3 gray-scale transformation method120666.1000686686666

12.9.4 Pseudo-color Processing in Frequency Domain 296

12.9.5 Pseudo-color Processing of Multispectral Images —— Commonly Used in Remote Sensing 296

12. 10 Summary 297

Chapter 13 Image Restoration 298

13. 1 Understanding image restoration 298

13. 1. 1 Causes of image blur 2998

13. 1.2 Repair model 299

The importance of 13. 1.3 PSF 299

13.2 blur and noise 300

13.3 image restoration using wiener filter 302

13.4 image restoration using traditional filters 306

13.5 image restoration using Lucy-Richardson algorithm

13.6 image restoration using blind deconvolution algorithm

13.7 summary 320

The third part of the image processing practice 32 1

Chapter 14 Application of Wavelet Transform in Images 322

14. 1 wavelet analysis basis 322

14.2 wavelet analysis technology 323

14.2. 1 continuous wavelet transform 323

14.2.2 discrete wavelet transform 325

14.2.3 wavelet reconstruction 327

14.3 wavelet image compression 328

14.4 wavelet image denoising 332

The basic principle of 14.4. 1 332

14.4.2 wavelet denoising example 332

14.5 wavelet image enhancement 336

14.6 wavelet image fusion 337

14.7 summary 340

Chapter 15 Image Segmentation 34 1

15. 1 Image Segmentation Basis 34 1

15. 1. 1 image segmentation clarity 34 1

15. 1.2 Overview of edge detection 342

15.2 edge detection operator 343

1 Roberts edge operator 343

15.2.2 Suo Beier edge operator 344

15.2.3 Prewitt edge operator 344

Laplace edge operator 345

15.2.5 Canny edge operator 345

15.2.6 MATLAB program realization 346

15.3 line extraction 349

15.3. 1 Hough transform method 349

15.3.2 MATLAB program to realize 350

15.4 threshold segmentation 353

15.4. 1 manual selection method 354

15.4.2 automatic threshold method 354

15.4.3 MATLAB program realization 357

15.5 watershed algorithm 359

15.6 regional growth and division merger 36 1

15.6. 1 regional growth method 36 1

15.6.2 Division and merger of regions

15.6.3 MATLAB quadtree decomposition363

15.7 Other segmentation methods 365

15.7. 1 color image segmentation 365

15.7.2 clustering algorithm 366

15.7.3 MATLAB program realization 366

15.8 Summary 369

Chapter 16 Image Representation and Description 370

16. 1 Basic concept of shape matching 370

16.2 shape means 37 1.

16.2. 1 chain code 37 1

16.2.2 Spline 372

Polygon approximation 372

Marking drawing 373

16.3 skeleton description 374

16.3. 1 skeleton representation 374

16.3.2 skeleton, refinement and central axis 375

16.3.3 skeleton algorithm 375

Realization of 16.3.4 skeleton with MATLAB program 375

The 16.4 shape descriptor 376 is based on geometric features.

1 dispersion 376

16.4.2 Euler number 377

16.4.3 Bump 377

16.4.4 Complexity 378

Eccentricity 378

16. 4. 6 MATLAB program to realize Euler number of binary image 378.

16.5 boundary descriptor 379

16.6 area description 380

16.6. 1 moment invariants

16.6.2 morphological description 38 1

16.6.3 MATLAB program realization 384

16.7 texture 385

16.7. 1 histogram statistical characteristics 386

16.7.2 Gray Difference Statistics Method 387

16.7.3 Image Gray Gradient Direction Matrix 388

16.7.4 autocorrelation function method 388

16.7.5 Fourier characteristics 389

16.7.6 Texture Analysis 390

Wavelet analysis 390

16.8 example of shape recognition 39 1

16.9 abstract 393

Chapter 17 Pattern Recognition 394

17. 1 pattern recognition 394

On the main theories and methods of17.1.6038+0 pattern recognition5000.000000000605

17. 1.2 pattern recognition process 397

17.2 statistical pattern recognition 397

1 statistical pattern recognition method 397

17.2.2 feature analysis 399

Feature extraction 40 1

17.2.4 function selection 402

17.2.5 Bayesian classifier 403

17.3 neural network identification 403

1 neuron model 403

17.3.2 network structure 404

The back propagation network 406

17.3.4 MATLAB Program to Realize Image Recognition 409

17.4 fuzzy identification411

17. 4. 1 image fuzziness

17.4.2 Basic concepts of fuzzy subsets 4 12

17.4.3 Basic terms and operations 4 13

17.4.4 ambiguity measurement method

Fuzzy pattern recognition 4 14

17.5 Summary 4 15

Chapter 18 MATLAB image application example 4 16

18. 1 image application domain 4 16

18.2 Biometric technology 4 17

Principle of fingerprint identification 4 17

18.2.2 facial recognition principle 4 18

18.2.3 fingerprint identification with MATLAB program.

18.3 Digital Watermarking Technology56866

18.3. 1 digital watermark application domain 426

18.3.2 Technical Features of Digital Watermarking 427

18.3.3 Digital Image Watermarking Algorithm 428

18.3.4 MATLAB program implementation 430

18.4 remote sensing image processing 432

18.4. 1 the characteristics of multispectral images58900.00000000105

18.4.2 MATLAB program realization 434

18.5 Summary 439

Chapter 19 MATLAB Tribological Simulation Based on Image 440

Image generation and representation of 19 tribological surface26438+065438 566437.10006006066

19. 1. 1 3D surface description specification 440

The description specification is19.1.2dem41

19.1.3 correlation between DEM model and the process of establishing three-dimensional geometry of friction surface 4 1

19.10.4 verification of the correctness of the reconstruction of tribological surface model506661

The digital rough surface 443 of 19. 1.5 is obtained by using the surface data generation technology.

19.10.6 generation of surface structure466636.666666666666

19.10.7 design of artificial micron-scale surface texture50000.00000000005

19.2 Acquisition of parameters related to tribology simulation calculation 449

19.3 tribology simulation calculation programming 452

Real surface-based contact model 452

Simulation calculation of surface temperature distribution 454

General program for simulating and calculating surface temperature distribution

19.4 image representation of tribology simulation results 457

19.5 Summary 460