Robust Computer Vision
Sebe N. Lew Michael
Sold by Majestic Books, Hounslow, United Kingdom
AbeBooks Seller since 19 January 2007
New - Soft cover
Condition: New
Ships from United Kingdom to U.S.A.
Quantity: 4 available
Add to basketSold by Majestic Books, Hounslow, United Kingdom
AbeBooks Seller since 19 January 2007
Condition: New
Quantity: 4 available
Add to basketPrint on Demand pp. 232 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.
Seller Inventory # 5824235
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.
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