Robust Computer Vision: Theory and Applications

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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 and many results, based on research by the authors and their collaborators, are presented. Although this book contains many up-to-date results, it is written in a style that should suit both experts and novices in computer vision.