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Proceedings Paper

Probabilistic approach to 3D inference of geons from a 2D view
Author(s): Alain Jacot-Descombes; Thierry Pun
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Paper Abstract

A new, probabilistic approach for inferring 3-D volumetric primitives from a single 2-D view is presented. This recognition relies on the assumption that every object can be decomposed into component parts that belong to a finite set or alphabet of volumetric primitives (geons). For each possible primitive from the permissible set, a conditional probability function is computed. This law specifies the probability of obtaining the primitive given an observable 2- D measure or feature. The distribution functions are determined by simulation, on the basis of a representative number of random projections of the primitives. The measures themselves are chosen in such a way that they can easily be extracted from real images and their discriminative power for the volumetric primitive inference is high. Examples illustrate the proposed approach.

Paper Details

Date Published: 1 March 1992
PDF: 10 pages
Proc. SPIE 1708, Applications of Artificial Intelligence X: Machine Vision and Robotics, (1 March 1992); doi: 10.1117/12.58603
Show Author Affiliations
Alain Jacot-Descombes, Univ. of Geneva (Switzerland)
Thierry Pun, Univ. of Geneva (Switzerland)


Published in SPIE Proceedings Vol. 1708:
Applications of Artificial Intelligence X: Machine Vision and Robotics
Kevin W. Bowyer, Editor(s)

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