Advanced Image and Video Processing Using MATLAB

Advanced Image and Video Processing Using MATLAB
اسم المؤلف
Shengrong Gong, Chunping Liu, Yi Ji, Baojiang Zhong, Yonggang Li, Husheng Dong
التاريخ
5 يوليو 2021
المشاهدات
التقييم
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Advanced Image and Video Processing Using MATLAB
Shengrong Gong, Chunping Liu, Yi Ji, Baojiang Zhong, Yonggang Li, Husheng Dong
Contents
Part I The Basic Concepts
1 Introduction 3
1.1 Basic Concepts and Terminology . 3
1.1.1 Digital Image and Digital Video . 3
1.1.2 Image Processing 6
1.1.3 Image Analysis . 6
1.1.4 Video Analysis 8
1.2 Image and Video Analysis 9
1.2.1 Image and Video Scene Segmentation . 9
1.2.2 Image and Video Feature Description . 10
1.2.3 Object Recognition in Images/Videos . 12
1.2.4 Scene Description and Understanding . 13
1.3 Examples of Advanced Applications 14
1.3.1 Image Correction 14
1.3.2 Image Fusion . 15
1.3.3 Digital Image Inpainting . 15
1.3.4 Image Stitching . 16
1.3.5 Digital Watermarking . 17
1.3.6 Visual Object Recognition . 18
1.3.7 Object Tracking . 20
1.3.8 Dynamic Scene Classification . 21
1.3.9 Pedestrian Re-identification . 22
1.3.10 Lip Recognition in Video 22
References 23
2 Matlab Functions of Image and Video 27
2.1 Introduction to MATLAB for Image and Video 27
2.2 Basic Elements of MATLAB 28
ix2.2.1 Working Environment 28
2.2.2 Data Types 29
2.2.3 Array and Matrix Indexing in MATLAB . 32
2.2.4 Standard Arrays . 34
2.2.5 Command-Line Operations . 34
2.3 Programming Tools: Scripts and Functions 35
2.3.1 M-Files . 35
2.3.2 Operators 36
2.3.3 Important Variables and Constants . 38
2.3.4 Number Representation 38
2.3.5 Flow Control . 39
2.3.6 Input and Output 41
2.4 Graphics and Visualization . 41
2.5 The Image Processing Toolbox 46
2.5.1 The Image Processing Toolbox: An Overview . 46
2.5.2 Essential Functions and Features . 47
2.5.3 Displaying Information About an Image File 52
2.5.4 Reading an Image File 52
2.5.5 Data Classes and Data Conversions . 53
2.5.6 Displaying the Contents of an Image 55
2.5.7 Exploring the Contents of an Image 57
2.5.8 Writing the Resulting Image onto a File . 58
2.6 Video Processing in MATLAB 58
2.6.1 Reading Video Files 59
2.6.2 Processing Video Files 59
2.6.3 Playing Video Files 60
2.6.4 Writing Video Files 61
2.6.5 Basic Digital Video Manipulation in MATLAB 62
References 63
3 Image and Video Segmentation 65
3.1 Introduction 65
3.2 Threshold Segmentation . 66
3.2.1 Global Threshold Image Segmentation . 68
3.2.2 Local Dynamic Threshold Segmentation . 69
3.3 Region-Based Segmentation 74
3.3.1 Region Growing . 74
3.3.2 Region Splitting and Merging . 78
3.4 Segmentation Based on Partial Differential Equation . 88
3.5 Image Segmentation Based on Clustering . 94
3.6 Image Segmentation Method Based on Graph Theory 97
3.6.1 Introduction 97
3.6.2 GraphCut and Improved Image Segmentation
Method . 99
x Contents3.7 Video Motion Region Extraction Method Based
on Cumulative Difference 107
References 111
4 Feature Extraction and Representation 113
4.1 Introduction 113
4.2 Histogram-Based Features 115
4.2.1 Grayscale Histogram . 115
4.2.2 Histograms of Oriented Gradients 117
4.3 Texture Features . 121
4.3.1 Haralick Texture Descriptors 122
4.3.2 Wavelet Texture Descriptors 126
4.3.3 LBP Texture Descriptors 131
4.4 Corner Feature Extraction 135
4.4.1 Moravec Algorithm 135
4.4.2 Harris Corner Detection Operator 137
4.4.3 SUSAN Corner Detection Algorithm 141
4.5 Local Invariant Feature Point Extraction 144
4.5.1 Local Invariant Point Feature of SURF 145
4.5.2 SIFT Scale-Invariant Feature Algorithm 149
References 158
Part II Advances in Image Processing
5 Image Correction 161
5.1 Introduction 161
5.2 Noise Reduction Using Spatial-Domain Techniques . 161
5.2.1 Selected Noise Probability Density Functions 162
5.2.2 Filtering . 168
5.3 Image Deblurring 173
5.3.1 The Restoration of Defocus Blurred Image . 174
5.3.2 Restoration of Motion Blurred Image . 176
5.4 Fisheye Distortion Correction Using Spherical Coordinates
Model 180
5.5 Skew Correction of Text Images . 186
5.5.1 Feature Analysis of Text Images . 187
5.5.2 The Basic Idea of Hough Transform 187
5.5.3 The Implementation Steps of Text Images Skew
Correction . 188
5.6 Image Dehazing Correction . 191
5.6.1 Single Image Dehazing 191
5.6.2 Dark Channel Prior 192
5.6.3 Implementation Steps of DCP . 194
5.6.4 Refine Transmission Map Using Soft Matting . 195
Contents xi5.7 Image Deraining Correction . 200
5.7.1 Related Work . 200
5.7.2 Single Image De-rain with Deep Detail Network . 200
5.7.3 Implementation of Image Deraining with Deep
Network 203
References 206
6 Image Inpainting . 209
6.1 Introduction 209
6.1.1 Structure Oriented Image Inpainting Technology . 210
6.1.2 Texture-Based Image Inpainting Technology 211
6.2 The Principle of Image Inpainting 211
6.3 Variational PDE-Based Image Inpainting . 213
6.3.1 Image Inpainting Algorithm Based on Total
Variational Model . 214
6.3.2 Image Inpainting Based on CDD Model . 219
6.4 Exemplar-Based Image Inpainting Algorithm 222
References 230
7 Image Fusion . 233
7.1 Introduction 233
7.2 Fusion Categories 234
7.2.1 Multi-view Fusion . 234
7.2.2 Multimodal Fusion . 236
7.2.3 Multi-temporal Fusion 240
7.2.4 Multi-focus Fusion . 242
7.3 Image Fusion Schemes 243
7.4 Image Fusion Using Wavelet Transform 248
7.4.1 Basis of Wavelet Transform 248
7.4.2 Discrete Dyadic Wavelet Transform of Image
and Its Mallat Algorithm 249
7.4.3 Steps of Implementation . 250
7.5 Region-Based Image Fusion 253
7.5.1 Basic Framework of Regional Integration 254
7.5.2 The Strategy of Regional Joint Representation . 255
7.5.3 The Rules of Fusion 256
7.5.4 Wavelet Fusion of Regional Variance . 256
7.6 Image Fusion Using Fuzzy Dempster-Shafer Evidence
Theory 260
7.7 Image Quality and Fusion Evaluations . 263
7.7.1 Subjective Evaluation of Image Fusion 264
7.7.2 Objective Evaluation of Image Fusion . 264
References 268
xii Contents8 Image Stitching 271
8.1 Introduction 271
8.2 Image Stitching Based on Region 272
8.2.1 Image Stitching Based on Ratio Matching 273
8.2.2 Image Stitching Based on Line and Plane Feature 276
8.2.3 Image Stitching Based on FFT 283
8.3 Images Stitching Based on Feature Points 290
8.3.1 SIFT Feature Points Detection . 290
8.3.2 Image Stitching Based on Harris Feature Points 297
8.3.3 Auto-Sorting for Image Sequence 304
8.3.4 Harris Point Registration Based on RANSAC
Algorithm . 307
8.4 Panoramic Image Stitching . 320
References 327
9 Image Watermarking . 329
9.1 Introduction 329
9.2 Fragile Watermarking Based on Spatial Domain 334
9.3 Robust Watermarking Based on DCT . 336
9.4 Semi-fragile Watermarking Based on DWT . 344
References 349
10 Visual Object Recognition 351
10.1 Face Recognition Based on Locality Preserving Projections . 351
10.2 Facial Expression Recognition Using PCA 375
10.3 Extraction and Recognition of Characters in Pictures 380
References 387
Part III Advances in Video Processing and then Associated Chapters
11 Visual Object Tracking 391
11.1 Adaptive Background Modeling by Using a Mixture
of Gaussians 391
11.2 Object Tracking Based on Ransac 396
11.3 Object Tracking Based on MeanShift 401
11.3.1 Description of the Object Model . 402
11.3.2 A Description of the Candidate Model . 402
11.3.3 Similarity Function . 403
11.3.4 Object Location . 403
11.4 Object Tracking Based on Particle Filter . 409
11.4.1 Prior Knowledge of the Goal . 410
11.4.2 System State Transition . 410
11.4.3 System Observation 411
11.4.4 Posterior Probability Calculation . 412
Contents xiii11.4.5 Particle Resampling 412
11.4.6 Implementation Steps . 413
11.5 Multiple Object Tracking 418
References 427
12 Dynamic Scene Classification Based on Topic Models 429
12.1 Overview 429
12.2 Introduction to the Topic Models . 430
12.2.1 LDA Model 430
12.2.2 TMBP Model Based on Factor Graph . 433
12.2.3 TMBP Model Fusing Prior Knowledge 436
12.3 Dynamic Scene Classification Based on TMBP 439
12.4 Behavior Recognition Based on LDA Topic Model . 451
13 Image Understanding-Person Re-identification 475
13.1 Introduction 475
13.2 Person Re-ID Scenarios 477
13.3 Methodology . 478
13.4 Public Datasets and Evaluation Metrics in Person
Re-identification . 480
13.4.1 Public Datasets 480
13.4.2 Evaluation Metrics . 483
13.5 Classic Feature Representations for Person
Re-identification . 484
13.5.1 Salient Color Names 484
13.5.2 Local Maximal Occurrence Representation . 487
13.6 An Example of Metric Learning Based Person
Re-identification Method-XQDA . 501
References 511
14 Image and Video Understanding Based on Deep Learning . 513
14.1 Introduction 513
14.2 Model Analysis of CNN . 515
14.2.1 Basic Modules of CNN . 515
14.2.2 Convolution and Pooling 515
14.2.3 Activation Function 516
14.2.4 Softmax Classifier and Cost Function . 517
14.2.5 Learning Algorithm 519
14.2.6 Dropout . 521
14.2.7 Batch Normalization 522
14.3 Typical CNN Models . 522
14.3.1 LeNet 522
xiv Contents14.3.2 AlexNet . 523
14.3.3 GoogLeNet 524
14.3.4 VGGNet 528
14.3.5 ResNet . 530
14.4 Deep Learning Model for Lip Recognition Instance . 531
14.4.1 Testing Dataset . 531
14.4.2 Deep Network Training . 532
14.4.3 Code Analysis 536
14.5 Deep CNN Architecture for Event Recognition Instance 539
14.5.1 Testing Dataset . 539
14.5.2 Deep Feature Extraction . 540
14.5.3 Spatial-Temporal Feature Fusion . 540
14.5.4 Fisher Vector Encoding . 541
14.5.5 Code Analysis 542
References 553
Appendix: Common Evaluation Criterion . 555
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