Robust Computer Vision: Theory and Applications by N. SebeRobust Computer Vision: Theory and Applications by N. Sebe

Robust Computer Vision: Theory and Applications

byN. Sebe, M.s. Lew

Paperback | December 6, 2010

Pricing and Purchase Info

$180.45 online 
$205.95 list price save 12%
Earn 902 plum® points

Prices and offers may vary in store

Quantity:

In stock online

Ships free on orders over $25

Not available in stores

about

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."

Nicu Sebe received his PhD degree from Leiden University in 2001. Currently, he is an Assistant Professor at Leiden University in the Netherlands. His main interest is in the fields of computer vision and pattern recognition, in particular content-based retrieval and robust techniques in computer vision. He was co-editing the proceedin...
Loading
Title:Robust Computer Vision: Theory and ApplicationsFormat:PaperbackDimensions:215 pagesPublished:December 6, 2010Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:9048162904

ISBN - 13:9789048162901

Reviews

Table of Contents

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.