A selective vision system sequentially collects evidence to answer a specific question with a desired level of confidence. Efficiency comes from processing the scene only where necessary, to the level of detail necessary, and with only the necessary operators. Knowledge representation and sequential decision making are central issues for selective vision, which takes advantage of prior knowledge of a domain's abstract and geometrical structure (e.g., “part-of” and “adjacent” relationships), and also uses information from a scene instance gathered during analysis. The TEA-1 selective vision system uses Bayes nets for representation, benefit-cost analysis for control of visual and nonvisual actions; and its data structures and decision-making algorithms provide a general, reusable framework. TEA-1 solves the T-world problem, an abstraction of a large set of scene domains and tasks. Some factors that affect the success of selective perception are analyzed by using TEA-1 to solve ensembles of randomly produced, simulated T-world problems. Experimental results with a real-world T-world problem, dinner table scenes, are also presented.
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