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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."
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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