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Instructors: Brian Curless (University of Washington), Harry Shum (Microsoft Research Asia), and Richard Szeliski (organizer, Microsoft Research).
Duration: 3.5 hours
This course is an introduction to the field of image-based rendering, which is an emerging area in computer graphics concerned with rendering real-world (and sometimes synthetic) images from large collections of captured or pre-rendered images. This area spans the fields of computer graphics, computer vision, and image processing, and is currently a very active inter-disciplinary research area in all of these communities.
Brian Curless is an associate professor of Computer Science and Engineering at the University of Washington. He received the B.S. degree in Electrical Engineering from the University of Texas at Austin, and, after working for a year at SRI International as a research engineer, he earned his M.S. and Ph.D. degrees in Electrical Engineering from Stanford University. Curless received a university-wide Outstanding Teaching Award while at Stanford, has been nominated for Washington's university-wide teaching award, and has co-organized and co-taught courses on 3D Photography at CVPR and SIGGRAPH. His research interests span several areas of computer vision and graphics, with emphasis on capturing and modeling shape and appearance.
Harry Shum received his Ph.D. in robotics from the School of Computer Science, Carnegie Mellon University in 1996. He worked as a researcher for three years in the vision technology group at Microsoft Research Redmond. In 1999, he moved to Microsoft Research Asia where he is currently a senior researcher and the assistant managing director. His research interests include computer vision, computer graphics, human computer interaction, pattern recognition, statistical learning and robotics. He is the General Co-Chair of Tenth International Conference on Computer Vision (ICCV 2005 Beijing).
Richard Szeliski is a Senior Researcher in the Interactive Visual Media Group at Microsoft Research, where he is pursuing research in 3-D computer vision, video scene analysis, and image-based rendering. He received a Ph. D. degree in Computer Science from Carnegie Mellon University, Pittsburgh, in 1988. Dr. Szeliski has published over 100 research papers in computer vision, computer graphics, medical imaging, neural nets, and parallel numerical algorithms, as well as the book Bayesian Modeling of Uncertainty in Low-Level Vision. He is on the editorial board of the International Journal of Computer Vision, and served as Program Chair for ICCV'2001, organizer of the ICCV'99 Workshop on Vision Algorithms, and Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence.