A Bayesian/Geometric Framework For Reconstructing 3-D Shapes In Robot Vision

Basilis Gidas; Jose Torreao

doi:10.1117/12.951670

17 May 1989 A Bayesian/Geometric Framework For Reconstructing 3-D Shapes In Robot Vision

Basilis Gidas, Jose Torreao

Proceedings Volume 1058, High Speed Computing II; (1989) https://doi.org/10.1117/12.951670
Event: OE/LASE '89, 1989, Los Angeles, CA, United States

Abstract

We introduce a new Bayesian/Geometric method for estimating orientation and depth from shade and/or texture. The method is based on a prior distribution on the space of all posible (random) surfaces, and a variant of the annealing algorithm for constraint global optimization problems. The method is also useful for partially classifying image contours.

Citation Download Citation

Basilis Gidas and Jose Torreao "A Bayesian/Geometric Framework For Reconstructing 3-D Shapes In Robot Vision", Proc. SPIE 1058, High Speed Computing II, (17 May 1989); https://doi.org/10.1117/12.951670

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