In order to help our readers fully understand the history, ideas, theories, improvement, simulation and features of video parsing algorithm, this book gives a detailed introduction to more than 50 basic theories of operators, descriptors, filtration, transformation, methods and so on concerning video parsing algorithm, and expounds the means for improvement and experimental simulation of video parsing algorithm. It systematically summarizes both its strong points and shortcomings, and provides sets of source codes of experimental simulation and video image libraries. Please find www.kedachang.com for related materials.
This book can be used astextbooks for junior andsenior undergraduates and graduates in disciplines like information，computer，automation，electronics and communications，be used as a reference book for research and development in fields likevideo analysis and intelligence identification
CONTENTS
PREFACE
Chapter 1 Video Image Enhancement 1
1.1 Recursive Median Filter 1
References 2
1.2 Least Squares Filter 2
1.3 Homomorphic Filter 7
References 10
1.4 Bilateral Filter 10
References 14
1.5 Guided Filter 14
References 16
1.6 Lateral Inhibition Network 16
References 22
1.7 Mathematical Morphology 22
References 30
Chapter 2 Video Image Segmentation 31
2.1 Double-Peak Histogram 31
References 34
2.2 Watershed 34
References 36
2.3 Regional Split-and-Merge 37
References 38
2.4 OTSU 38
References 40
2.5 Maximum 2D Entropy 41
References 47
2.6 2D Cross-Entropy 48
References 55
Chapter 3 Key Point Detection 56
3.1 Moravec 56
References 58
3.2 Forstner 58
References 60
3.3 Harris 60
References 64
3.4 SUSAN 64
References 69
3.5 CSS 69
References 74
3.6 FAST 74
References 77
3.7 DoG 77
References 80
3.8 LoG 80
References 83
Chapter 4 Visual Feature Descriptors 84
4.1 Hu Moment 84
References 86
4.2 Legendre Moments 87
4.3 Fourier Descriptors 89
References 91
4.4 Haar 91
References 96
4.5 HOG 96
References 98
4.6 LBP 99
References 103
4.7 SIFT 103
References 112
4.8 SURF 112
References 116
Chapter 5 Transform and Dimension Reduction 117
5.1 K-L Transform 117
References 119
5.2 DCT Transform 120
References 126
5.3 Gabor Transform 126
References 129
5.4 Wavelet Transform 129
References 135
5.5 Haar Transform 136
References 140
5.6 Hough Transform 140
References 146
5.7 LPT Transform 146
References 150
5.8 PCA 150
References 154
5.9 LDA 154
References 157
Chapter 6 Clustering and Classification 158
6.1 Measure Methods of Similarity 158
6.2 K-Means Clustering 162
References 165
6.3 Bayesian Methods 165
References 168
6.4 Adaptive Boosting 168
References 174
6.5 SVM 174
References 181
Chapter 7 Motion Detection and Target Tracking 182
7.1 Background Subtraction 182
References 189
7.2 Temporal Difference 189
References 194
7.3 Optical Flow 194
References 202
7.4 Kalman Filtering 202
References 207
7.5 Mean Shift 207
References 211
7.6 CamShift Method 211
References 213
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