Robust Computer Vision: Theory and Applications (Computational Imaging and Vision)

Foreword......Page 12
Preface......Page 14
1. INTRODUCTION......Page 18
1 Visual Similarity......Page 19
1.1 Color......Page 21
1.2 Texture......Page 24
1.3 Shape......Page 26
1.4 Stereo......Page 28
1.6 Facial expression......Page 30
1.7 Summary......Page 32
2 Evaluation of Computer Vision Algorithms......Page 33
3 Overview of the Book......Page 36
1 Introduction......Page 42
2 Statistical Distributions......Page 43
2.1 Gaussian Distribution......Page 44
2.2 Exponential Distribution......Page 55
2.3 Cauchy Distribution......Page 58
3 Robust Statistics......Page 60
3.1 Outliers......Page 61
4 Maximum Likelihood Estimators......Page 62
5 Maximum Likelihood in Relation to Other Approaches......Page 64
6 Our Maximum Likelihood Approach......Page 67
6.1 Scale Parameter Estimation in a Cauchy Distribution......Page 71
7 Experimental Setup......Page 74
8 Concluding Remarks......Page 76
1 Introduction......Page 78
3 Color Models......Page 81
3.1 RGB Color System......Page 82
3.2 HSV Color System......Page 83
3.3 l1l2l3 Color System......Page 84
4 Color Based Retrieval......Page 85
4.1 Color Indexing......Page 86
5.1 Early Experiments......Page 90
5.2 Usability Issues......Page 91
5.3 Printer-Scanner Noise Experiments......Page 92
5.5 Quantization......Page 93
5.6 Distribution Analysis......Page 94
6 Experiments with the Objects Database......Page 96
7 Concluding Remarks......Page 98
1 Introduction......Page 100
2 Human Perception of Texture......Page 103
3 Texture Features......Page 104
3.1 Texture Distribution Models......Page 105
3.1.3 Center-symmetric covariance measures......Page 106
3.1.5 Complementary feature pairs......Page 108
3.2 Gabor and Wavelet Models......Page 109
4 Texture Classification Experiments......Page 112
4.2 Distribution Analysis......Page 114
4.3 Misdetection Rates......Page 116
5 Texture Retrieval Experiments......Page 121
5.1 Texture Features......Page 122
5.3 Similarity Noise for QMF-Wavelet Transform......Page 123
5.4 Similarity Noise for Gabor Wavelet Transform......Page 125
6 Concluding Remarks......Page 126
1 Introduction......Page 128
2 Human Perception of Visual Form......Page 130
3 Active Contours......Page 135
3.1 Behavior of Traditional Active Contours......Page 137
3.2 Generalized Force Balance Equations......Page 141
3.3 Gradient Vector Flow......Page 142
4 Invariant Moments......Page 147
5 Experiments......Page 148
6 Conclusions......Page 151
1 Introduction......Page 152
1.1 Stereoscopic Vision......Page 154
2 Stereo Matching......Page 155
2.1 Related Work......Page 159
3.1 Template Based Algorithm......Page 161
3.2 Multiple Windows Algorithm......Page 163
3.3 Cox’ Maximum Likelihood Algorithm......Page 164
4 Stereo Matching Experiments......Page 167
4.2 Stereo Matching Results......Page 168
5 Motion Tracking Experiments......Page 174
6 Concluding Remarks......Page 177
7. FACIAL EXPRESSION RECOGNITION......Page 180
1 Introduction......Page 181
2 Emotion Recognition......Page 183
2.2 Review of Facial Expression Recognition......Page 184
3 Face Tracking and Feature Extraction......Page 188
4 The Static Approach: Bayesian Network Classifiers......Page 190
4.1 Continuous Naive-Bayes: Gaussian and Cauchy Naive Bayes Classifiers......Page 192
4.2 Beyond the Naive-Bayes Assumption: Finding Dependencies among Features Using a Gaussian TAN Classifier......Page 193
5 The Dynamic Approach: Expression Recognition Using Multi-level HMMs......Page 196
5.1 Hidden Markov Models......Page 199
5.2 Expression Recognition Using Emotion-Specific HMMs......Page 200
5.3 Automatic Segmentation and Recognition of Emotions Using Multi-level HMM.......Page 201
6 Experiments......Page 204
6.1.1 Person-Dependent Tests......Page 208
6.1.2 Person-Independent Tests......Page 210
6.2 Results Using the Cohn-Kanade Database......Page 211
7 Summary and Discussion......Page 212
References......Page 216
Index......Page 228