| Foreword | p. xi |
| Preface | p. xiii |
| Introduction | p. 1 |
| Visual Similarity | p. 2 |
| Color | p. 4 |
| Texture | p. 7 |
| Shape | p. 9 |
| Stereo | p. 11 |
| Motion | p. 13 |
| Facial expression | p. 13 |
| Summary | p. 15 |
| Evaluation of Computer Vision Algorithms | p. 16 |
| Overview of the Book | p. 19 |
| Maximum Likelihood Framework | p. 25 |
| Introduction | p. 25 |
| Statistical Distributions | p. 26 |
| Gaussian Distribution | p. 27 |
| Exponential Distribution | p. 38 |
| Cauchy Distribution | p. 41 |
| Robust Statistics | p. 43 |
| Outliers | p. 44 |
| Maximum Likelihood Estimators | p. 45 |
| Maximum Likelihood in Relation to Other Approaches | p. 47 |
| Our Maximum Likelihood Approach | p. 50 |
| Scale Parameter Estimation in a Cauchy Distribution | p. 54 |
| Experimental Setup | p. 57 |
| Concluding Remarks | p. 59 |
| Color Based Retrieval | p. 61 |
| Introduction | p. 61 |
| Colorimetry | p. 64 |
| Color Models | p. 64 |
| RGB Color System | p. 65 |
| HSV Color System | p. 66 |
| l[subscript 1]l[subscript 2]l[subscript 3] Color System | p. 67 |
| Color Based Retrieval | p. 68 |
| Color Indexing | p. 69 |
| Experiments with the Corel Database | p. 73 |
| Early Experiments | p. 73 |
| Usability Issues | p. 74 |
| Printer-Scanner Noise Experiments | p. 75 |
| Color Model | p. 76 |
| Quantization | p. 76 |
| Distribution Analysis | p. 77 |
| Experiments with the Objects Database | p. 79 |
| Concluding Remarks | p. 81 |
| Robust Texture Analysis | p. 83 |
| Introduction | p. 83 |
| Human Perception of Texture | p. 86 |
| Texture Features | p. 87 |
| Texture Distribution Models | p. 88 |
| Gray-level differences | p. 89 |
| Laws' texture energy measures | p. 89 |
| Center-symmetric covariance measures | p. 89 |
| Local binary patterns and trigrams | p. 91 |
| Complementary feature pairs | p. 91 |
| Gabor and Wavelet Models | p. 92 |
| Texture Classification Experiments | p. 95 |
| Distribution Analysis | p. 97 |
| Misdetection Rates | p. 99 |
| Summary | p. 104 |
| Texture Retrieval Experiments | p. 104 |
| Texture Features | p. 105 |
| Experiments Setup | p. 106 |
| Similarity Noise for QMF-Wavelet Transform | p. 106 |
| Similarity Noise for Gabor Wavelet Transform | p. 108 |
| Concluding Remarks | p. 109 |
| Shape Based Retrieval | p. 111 |
| Introduction | p. 111 |
| Human Perception of Visual Form | p. 113 |
| Active Contours | p. 118 |
| Behavior of Traditional Active Contours | p. 120 |
| Generalized Force Balance Equations | p. 124 |
| Gradient Vector Flow | p. 125 |
| Invariant Moments | p. 130 |
| Experiments | p. 131 |
| Conclusions | p. 134 |
| Robust Stereo Matching and Motion Tracking | p. 135 |
| Introduction | p. 135 |
| Stereoscopic Vision | p. 137 |
| Stereo Matching | p. 138 |
| Related Work | p. 142 |
| Stereo Matching Algorithms | p. 144 |
| Template Based Algorithm | p. 144 |
| Multiple Windows Algorithm | p. 146 |
| Cox' Maximum Likelihood Algorithm | p. 147 |
| Stereo Matching Experiments | p. 150 |
| Stereo Sets | p. 151 |
| Stereo Matching Results | p. 151 |
| Summary | p. 157 |
| Motion Tracking Experiments | p. 157 |
| Concluding Remarks | p. 160 |
| Facial Expression Recognition | p. 163 |
| Introduction | p. 164 |
| Emotion Recognition | p. 166 |
| Judgment Studies | p. 167 |
| Review of Facial Expression Recognition | p. 167 |
| Face Tracking and Feature Extraction | p. 171 |
| The Static Approach: Bayesian Network Classifiers | p. 173 |
| Continuous Naive-Bayes: Gaussian and Cauchy Naive Bayes Classifiers | p. 175 |
| Beyond the Naive-Bayes Assumption: Finding Dependencies among Features Using a Gaussian TAN Classifier | p. 176 |
| The Dynamic Approach: Expression Recognition Using Multi-level HMMs | p. 179 |
| Hidden Markov Models | p. 182 |
| Expression Recognition Using Emotion-Specific HMMs | p. 183 |
| Automatic Segmentation and Recognition of Emotions Using Multi-level HMM | p. 184 |
| Experiments | p. 187 |
| Results Using the Chen Database | p. 191 |
| Person-Dependent Tests | p. 191 |
| Person-Independent Tests | p. 193 |
| Results Using the Cohn-Kanade Database | p. 194 |
| Summary and Discussion | p. 195 |
| References | p. 199 |
| Index | p. 210 |
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