Robust Computer Vision: Theory and Applications: 26 (Computational Imaging and Vision, 26) - Softcover
Synopsis
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."
"synopsis" may belong to another edition of this title.
About the Author
Nicu Sebe received his PhD degree from Leiden University in 2001. Currently, he is an Assistant Professor at Leiden University in the Netherlands. His main interest is in the fields of computer vision and pattern recognition, in particular content-based retrieval and robust techniques in computer vision. He was co-editing the proceedings of the International Conference on Image and Video Retrieval 2002. He is also acting as the technical program co-chair for the International Conference on Image and Video Retrieval 2003.
Michael S. Lew received his PhD degree in Electrical Engineering from the University of Illinois at Urbana-Champaign. He is currently an Associate Professor at Leiden University in the Netherlands. He has published over 100 scientific papers and helped organize several large conferences including IEEE Multimedia, ACM Multimedia, and the International Conference on Image and Video Retrieval.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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.' 236 pp. Englisch. Seller Inventory # 9789048162901
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Taschenbuch. Condition: Neu. Robust Computer Vision | Theory and Applications | N. Sebe (u. a.) | Taschenbuch | xv | Englisch | 2010 | Springer | EAN 9789048162901 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 107245233
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.'Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 236 pp. Englisch. Seller Inventory # 9789048162901
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