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Proceedings Paper

Bayes nets for selective perception and data fusion
Author(s): Christopher R. Brown; Mauricio Marengoni; George Kardaras
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Paper Abstract

Selective perception sequentially collects evidence to support a specified hypothesis about a scene, as long as the additional evidence is worth the effort of obtaining it. Efficiency comes from selecting the best scene locations, resolution, and vision operators, where `best' is defined as some function of benefit and cost (typically, their ratio or difference). Selective vision implies knowledge about the scene domain and the imaging operators. We use Bayes nets for representation and benefit-cost analysis in a selective vision system with both visual and non-visual actions in real and simulated static and dynamic environments. We describe sensor fusion, dynamic scene, and multi-task applications.

Paper Details

Date Published: 31 January 1995
PDF: 11 pages
Proc. SPIE 2368, 23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities, (31 January 1995); doi: 10.1117/12.200788
Show Author Affiliations
Christopher R. Brown, Univ. of Rochester (United States)
Mauricio Marengoni, Univ. of Rochester (United States)
George Kardaras, Univ. of Rochester (United States)


Published in SPIE Proceedings Vol. 2368:
23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities
Peter J. Costianes, Editor(s)

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