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Proceedings chapter
English

A probabilistic approach to 3-D inference of geons from a 2-D view

Contributors

Jacot-Descombes, Alain; Pun, Thierry

Presented at

Orlando (FLA, USA)

Published in

Kevin W. Bowyer (Ed.), Applications of Artificial Intelligence X: Machine Vision and Robotics, p. 579-588

Collection

  • SPIE Proceedings; 1708

Publication date

1992

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.

UNIGE affiliation entities

UNIGE research groups

Citation (ISO format)

JACOT-DESCOMBES, Alain, PUN, Thierry. A probabilistic approach to 3-D inference of geons from a 2-D view. In: Applications of Artificial Intelligence X: Machine Vision and Robotics. Kevin W. Bowyer (Ed.). Orlando (FLA, USA). [s.l.] : [s.n.], 1992. p. 579–588. (SPIE Proceedings) doi: 10.1117/12.58603

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Technical informations

Creation

06/03/2015 17:12:13

First validation

06/03/2015 17:12:13

Update

27/01/2026 15:47:58

Status update

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Last indexation

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