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Robust Computer Vision [electronic resource] : 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 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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| Main Authors: | , , |
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| Format: | Texto biblioteca |
| Language: | eng |
| Published: |
Dordrecht : Springer Netherlands : Imprint: Springer,
2003
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| Subjects: | Computer science., Data structures (Computer science)., Information storage and retrieval., Multimedia information systems., Artificial intelligence., Computer graphics., Computer Science., Computer Imaging, Vision, Pattern Recognition and Graphics., Artificial Intelligence (incl. Robotics)., Data Structures, Cryptology and Information Theory., Multimedia Information Systems., Information Storage and Retrieval., |
| Online Access: | http://dx.doi.org/10.1007/978-94-017-0295-9 |
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