Date: Monday, May 17, 2004
Time: 11:30 AM
Room: NAC 8/206 (Csc Dept. Conference Room)
Speaker: Michael D. Grossberg
The goal of computational vision is to be able to determine properties of a scene from images. Over the past decade algorithms have been developed that can recover 3D scene structure, determine material properties, recover the lighting of a scene, track objects in motion, and recognize objects such as faces. Our ability to determining scene properties assumes that we can interpret the relationship between the images a camera produces and a scene. This relationship depends on properties of the camera. This talk will present geometric as well as photometric models for cameras. These models make it possible to determine the properties of the camera from images. Insights from these models lead to methods for novel camera design. These models can also be used to describe projectors, since they can be considered dual to cameras. Using these models, a projector-camera system that can control the appearance of objects will be described. The models and systems in this talk have applications in vision, graphics as well as to human-computer interfaces.
Michael D. Grossberg is a Research Scientist with the Columbia Automated Vision Environment (CAVE), at Columbia University. He received his PhD in Mathematics from the Massachusetts Institute of Technology in 1991. His research in computer vision has included topics in the geometric and photometric modeling of cameras, and analyzing features for indexing. Dr. Grossberg was a Lecturer in the Computer Science Department at Columbia University. He was also a Ritt Assistant Professor of Mathematics at Columbia University. He has held postdoctoral fellowships at the Max Plank Institute for Mathematics in Bonn, and Hebrew University in Jerusalem. He has authored and coauthored papers that have appeared in ICCV, ECCV, CVPR. He has filed several U.S. and international patents for inventions related to computer vision.