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2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 1
Geometric Distributions for Catadioptric Sensor Design
Kauai, Hawaii
December 08-December 14
ISBN: 0-7695-1272-0
R. Andrew Hicks, Drexel University
Ronald K. Perline, Drexel University
We present a general method of catadioptric sensor design for realizing prescribed projections. Our method makes use of geometric distributions in 3-dimensional space, which are generalizations of vector fields. The main idea is this: if one desires a reflective surface that will image the world in a certain way, then this condition determines the orientation of the tangent planes to the surface. Analytically, this means that the surface will then be determined by a pair of partial differential equations, which may or may not have a common solution. We show how to check if a common solution exists. If no common solution exists, we describe a method for obtaining optimal approximate solutions in a least-squares sense. As an example application, we construct a mirror that will give a panoramic view of a scene without any digital unwarping.
Citation:
R. Andrew Hicks, Ronald K. Perline, "Geometric Distributions for Catadioptric Sensor Design," cvpr, vol. 1, pp.584, 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 1, 2001
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