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Michael Grossberg
City College, City University of New York
Friday, Nov. 12, 11:00am
LC 102, Brooklyn Campus, Polytechnic University
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.
Bio:
Michael D. Grossberg is an Assistant Professor of Computer Science at City College, City University of New York. He spent four years as 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. For further information please contact Joshua Gluckman