Robust Computer Vision

Theory and Applications

By: N. Sebe, Michael Lew

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Paperback | 6 December 2010

At a Glance

Paperback
232 Pages

Dimensions(cm)
23.39 x 15.6 x 1.25

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$169.75

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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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