Bayes nets for selective perception and data fusion

Christopher R. Brown; Mauricio Marengoni; George Kardaras

doi:10.1117/12.200788

31 January 1995 Bayes nets for selective perception and data fusion

Christopher R. Brown, Mauricio Marengoni, George Kardaras

Proceedings Volume 2368, 23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities; (1995) https://doi.org/10.1117/12.200788
Event: 23 Annual AIPR Workshop: Image and Information Systems: Applications and Opportunities, 1994, Washington, DC, United States

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.

Citation Download Citation

Christopher R. Brown, Mauricio Marengoni, and George Kardaras "Bayes nets for selective perception and data fusion", Proc. SPIE 2368, 23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities, (31 January 1995); https://doi.org/10.1117/12.200788

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