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.
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