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 framework, Bayesian Networks, and Hidden Mar...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
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Dordrecht
Springer Netherlands
2003, 2003
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| Edition: | 1st ed. 2003 |
| Series: | Computational Imaging and Vision
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
| Summary: | 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." |
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| Physical Description: | XV, 215 p online resource |
| ISBN: | 9789401702959 |