Control of Selective Perception Using Bayes Nets and Decision Theory

A selective vision system sequentially collects evidence to support a specied hy-pothesis about a scene, as long as the additional evidence is worth the eort of obtaining it. EÆciency comes from processing the scene only where necessary, to the level of detail necessary, and with only the necessary operators. Knowledge repre-sentation and sequential decision-making are central issues for selective vision, which takes advantage of prior knowledge of a domain's abstract and geometrical structure and models for the expected performance and cost of visual operators. The TEA-1 selective vision system uses Bayes nets for representation and benet-cost analysis for control of visual and non-visual actions. It is the high-level control for an active vision system, enabling purposive behavior, the use of qualitative vision modules and a pointable multiresolution sensor. TEA-1 demonstrates that Bayes nets and decision theoretic techniques provide a general, re-usable framework for constructing computer vision systems that are selective perception systems, and that Bayes nets provide a general framework for representing visual tasks. Control, o