Robust computer vision : theory and applications
著者
書誌事項
Robust computer vision : theory and applications
（Computational imaging and vision, v. 26）
Kluwer Academic, c2003
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注記
Includes bibliographical references (p. [199]-209) and index
内容説明・目次
内容説明
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 framework, Bayesian Networks, and Hidden Markov models are also used. Three aspects are stressed: features, similarity metric, and models. Many interesting and important new results, based on research by the authors and their collaborators, are presented.
Although this book contains many new results, it is written in a style that suits both experts and novices in computer vision."
目次
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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