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