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

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Published in Kevin W. Bowyer. Applications of Artificial Intelligence X: Machine Vision and Robotics. Orlando (FLA, USA). 1992, p. 579-588
Collection SPIE Proceedings; 1708
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.
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Research group Computer Vision and Multimedia Laboratory
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JACOT-DESCOMBES, Alain, PUN, Thierry. A probabilistic approach to 3-D inference of geons from a 2-D view. In: Kevin W. Bowyer (Ed.). Applications of Artificial Intelligence X: Machine Vision and Robotics. Orlando (FLA, USA). [s.l.] : [s.n.], 1992. p. 579-588. (SPIE Proceedings; 1708) https://archive-ouverte.unige.ch/unige:47772

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Deposited on : 2015-03-06

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