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Robust Computer Vision - N. Sebe, M.S. Lew

Robust Computer Vision

Theory and Applications

, (Autoren)

Buch | Hardcover
215 Seiten
2003
Springer-Verlag New York Inc.
978-1-4020-1293-8 (ISBN)
CHF 149,75 inkl. MwSt
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."

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.

1. Introduction.- 2. Maximum Likelihood Framework.- 3. Color Based Retrieval.- 4. Robust Texture Analysis.- 5. Shape Based Retrieval.- 6. Robust Stereo Matching and Motion Tracking.- 7. Facial Expression Recognition.- References.

Reihe/Serie Computational Imaging and Vision ; 26
Zusatzinfo XV, 215 p.
Verlagsort New York, NY
Sprache englisch
Maße 210 x 297 mm
Themenwelt Informatik Grafik / Design Digitale Bildverarbeitung
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
ISBN-10 1-4020-1293-4 / 1402012934
ISBN-13 978-1-4020-1293-8 / 9781402012938
Zustand Neuware
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