2010, ISBN: 9789048162901

Kartoniert, 236 Seiten, 244mm x 170mm x 13mm, Sprache(n): eng From the foreword by Thomas Huang: "During the past decade, researchers in computer vision have found that probabilistic mac… Meer...

Kartoniert, 236 Seiten, 244mm x 170mm x 13mm, Sprache(n): eng 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."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. Versandkostenfreie Lieferung Computervision, Database, Emotion, emotionrecognition, Learning, machinelearning, Perception, Activecontour, Bayesiannetwork, hiddenmarkovmodel, Stereo, Textur, algorithms, classification, Cognition<

2013, ISBN: 9048162904

[SR: 17726967], Paperback, [EAN: 9789048162901], Springer, Springer, Book, [PU: Springer], 2013-10-04, Springer, From the foreword by Thomas Huang: "During the past decade, researchers in… Meer...

[SR: 17726967], Paperback, [EAN: 9789048162901], Springer, Springer, Book, [PU: Springer], 2013-10-04, Springer, 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." , 280291, Intelligence & Semantics, 3887, AI & Machine Learning, 3508, Computer Science, 5, Computers & Technology, 1000, Subjects, 283155, Books, 132552011, Computer Vision & Pattern Recognition, 3887, AI & Machine Learning, 3508, Computer Science, 5, Computers & Technology, 1000, Subjects, 283155, Books, 107197011, Information Theory, 3508, Computer Science, 5, Computers & Technology, 1000, Subjects, 283155, Books, 3747, Networks, 3740, Networks, Protocols & APIs, 3652, Networking & Cloud Computing, 5, Computers & Technology, 1000, Subjects, 283155, Books, 10806617011, Storage & Retrieval, 377894011, Network Administration, 3652, Networking & Cloud Computing, 5, Computers & Technology, 1000, Subjects, 283155, Books, 3525, Web Design, 172400, Dreamweaver, 295228, Fireworks, 295229, Flash, 3510, Web Development & Design, 5, Computers & Technology, 1000, Subjects, 283155, Books, 491300, Artificial Intelligence, 468204, Computer Science, 465600, New, Used & Rental Textbooks, 2349030011, Specialty Boutique, 283155, Books<

ISBN: 9789048162901

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 de… Meer...

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. New Textbooks>Trade Paperback>Technology>Xxxsoftware Engr>* Desc Unknown, Springer Netherlands Core >1 >T<

2010, ISBN: 9789048162901

Springer, Paperback, Auflage: Softcover reprint of the original 1st ed. 2003, 230 Seiten, Publiziert: 2010-12-06T00:00:01Z, Produktgruppe: Book, Hersteller-Nr.: 9789048162901, 0.33 kg, AI… Meer...

Springer, Paperback, Auflage: Softcover reprint of the original 1st ed. 2003, 230 Seiten, Publiziert: 2010-12-06T00:00:01Z, Produktgruppe: Book, Hersteller-Nr.: 9789048162901, 0.33 kg, AI & Machine Learning, Computer Science, Computing & Internet, Subjects, Books, Information Systems, Data Warehousing, Data Storage & Management, Databases, Database Management Systems, Algorithms, Programming, Graphics & Multimedia, Introduction to Programming, Software & Graphics, Reference, Digital Art, Arts & Humanities, Undergraduate & Postgraduate, 3e708b60-20fb-41e8-a18d-05c77fb89b4c_6901, 3e708b60-20fb-41e8-a18d-05c77fb89b4c_0, Arborist Merchandising Root, Springer, 2010<