One Microsoft Way
Redmond, WA 98052-6399
Tel: 425 706-4774
Fax: 425 706-7329
(Directions to our building)
In February 2015, I was elected to the National Academy of Engineering.
My computer vision textbook was published in November 2010 by Springer.
My research areas include computer vision and computer graphics.
My particular interests are in using vision to automatically build 3-D models from images, computational photography, and image-based rendering. I have worked on both traditional 3-D volumetric and surface model reconstruction, and on high-resolution image mosaic construction. I am also interested in using computer vision for human-computer interaction and for analysing image databases. I have additional research interests in geometric modeling, motion estimation, multiresolution algorithms and representations, and optimization algorithms.
Middlebury Stereo Vision Page (with Daniel Scharstein): an online evaluation of the best two-image stereo matching algorithms, along with test images, ground truth, and software.
MRF Energy Minimization Page (with Ramin Zabih, Daniel Scharstein, Olga Veksler, Vladimir Kolmogorov, Aseem Agarwala, Marshall Tappen, and Carsten Rother): a comparison of 2-D MRF energy minimization algorithms, along with test images, data files, and software.
Multi-View Stereo Evaluation (with Steve Seitz, Brian Curless, James Diebel, and Daniel Scharstein): a comparison of multi-view stereo 3D surface/volume reconstruction algorithms, along with calibrated test images.
Optical Flow Evaluation (with Simon Baker, Daniel Scharstein, JP Lewis, Stefan Roth, and Michael Black): a comparison of optic flow (motion estimation) algorithms, along with calibrated test sequences.
I was a founding editor of Foundations and Trends(R) in Computer Graphics and Vision. If you are interested in contributing a survey article, please contact one of the Editors-in-Chief.
University of Washington
Graphics and Imaging Laboratory
Digital Equipment Corporation's Cambridge Research Laboratory
SRI International's AI Center (Perception Program)
Carnegie Mellon Vision and Autonomous Systems Center
(Computer Vision Home Page)
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