Robust Computer Vision

Beschreibung

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."

Autor*in

N. Sebe

Themen in »Robust Computer Vision«

Active contour Bayesian network Hidden Markov Model Stereo Textur algorithms classification cognition computer vision database emotion emotion recognition learning machine learning perception

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