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Foreword. Preface.
1: Introduction. 1. Visual Similarity. 2. Evaluation of Computer Vision Algorithms. 3. Overview of the Book.
2: Maximum Likelihood Framework. 1. Introduction. 2. Statistical Distributions. 3. Robust Statistics. 4. Maximum Likelihood Estimators. 5. Maximum Likelihood in Relation to Other Approaches. 6. Our Maximum Likelihood Approach. 7. Experimental Setup. 8. Concluding Remarks.
3: Color Based Retrieval. 1. Introduction. 2. Colorimetry. 3. Color Models. 4. Color Based Retrieval. 5. Experiments with the Corel Database. 6. Experiments with the Objects Database. 7. Concluding Remarks.
4: Robust Texture Analysis. 1. Introduction. 2. Human Perception of Texture. 3. Texture Features. 4. Texture Classification Experiments. 5. Texture Retrieval Experiments. 6. Concluding Remarks.
5: Shape Based Retrieval. 1. Introduction. 2. Human Perception of Visual Form. 3. Active Contours. 4. Invariant Movements. 5. Experiments. 6. Conclusions.
6: Robust Stereo Matching and Motion Tracking. 1. Introduction. 2. Stereo Matching. 3. Stereo Matching Algorithms. 4. Stereo Matching Experiments. 5. Motion Tracking Experiments. 6. Concluding Remarks.
7: Facial Expression Recognition. 1. Introduction. 2. Emotion Recognition. 3. Face Tracking and Feature Extraction. 4. The Static Approach: Bayesian Network Classifiers. 5. The Dynamic Approach: Expression Recognition Using Multi-level HMM. 6. Experiments. 7. Summary and Discussion.
References. Index.
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Add Robust Computer Vision: Theory and Applications, From the foreword by Thomas Huang: During the past decade, researchers in computer vision have found that probabilistic machine learning methods are extremely powerful. This book describes some of these methods. In addition to the Maximum Likelihood , Robust Computer Vision: Theory and Applications to the inventory that you are selling on WonderClubX
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Add Robust Computer Vision: Theory and Applications, From the foreword by Thomas Huang: During the past decade, researchers in computer vision have found that probabilistic machine learning methods are extremely powerful. This book describes some of these methods. In addition to the Maximum Likelihood , Robust Computer Vision: Theory and Applications to your collection on WonderClub |