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Proceedings Paper

Estimation By Multiple Views Of Outdoor Terrain Modeled By Stochastic Processes
Author(s): Francois G. Amblard; David B. Cooper; Bruno Cernuschi-Frias
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Paper Abstract

An approach to 3-D surface estimation is introduced where surfaces are modelled as patches of primitive parameterized surfaces, and these parameters are estimated from images taken by cameras in two or more positions. Appropriate parameterized patches for modelling manufactured objects include planar, cylindrical, spherical and unrestricted quadric patches. Any parameterized function can be used. A complex 3-D object can be described as a collection of such patches. For more irregular surfaces such as outdoor terrain, we use a collection of primitive patches linked together as a stochastic process. More specifically, in this paper we use planar primitive patches, and describe their dependence by Markov Random Fields. We then treat 3-D terrain height estimation as stochastic pro-cess estimation given two or more image data sets. To the extent that the models used are appropriate, this should result in the most accurate surface height estimation, object recognition for objects such as boulders, crevices, mountains, etc, occulded surface height estimation, and other information extraction. A simple geometric explanation is given for the estimation algorithm. This paper is one aspect of a new Bayesian approach to stereo vision as presented in [4,5,6].

Paper Details

Date Published: 27 March 1987
PDF: 10 pages
Proc. SPIE 0726, Intelligent Robots and Computer Vision V, (27 March 1987); doi: 10.1117/12.937710
Show Author Affiliations
Francois G. Amblard, Brown University (United States)
David B. Cooper, Brown University (United States)
Bruno Cernuschi-Frias, University of Buenos Aires (Argentina)

Published in SPIE Proceedings Vol. 0726:
Intelligent Robots and Computer Vision V
David P. Casasent, Editor(s)

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