Titlebook: Robust Computer Vision; Theory and Applicati Nicu Sebe,Michael S. Lew Book 2003 Springer Science+Business Media Dordrecht 2003 Active conto

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Book 2003re 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 rese

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Maximum Likelihood Framework, the probability density function which maximizes the similarity probability. Furthermore, we illustrate our approach based on maximum likelihood which consists of finding the best metric to be used in an application when the ground truth is provided.

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1381-6446 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, bas

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Nicu Sebe,Michael S. Lewof the emerging digital economy. We do so by examining eight salient topics in electronic commerce (EC). Each of these topics is examined in detail in a separate section of this book.978-3-540-67344-6978-3-642-58327-8Series ISSN 2627-8510 Series E-ISSN 2627-8529

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Robust Texture Analysis,distribution models for extracting features as in the work by Ojala et al. [Ojala et al., 1996] . Secondly, we consider a texture retrieval application where we extract random samples from all the 112 original Brodatz’s textures and the goal is to retrieve samples extracted from the same original te