Visual behavior: modeling 'hidden' purposes in motion
Shaogang Gong · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
The effectiveness and usefulness of vision lies in its purposive, behavioral characteristics. The Bayesian belief revision theory is examined for effective modeling of integrated knowledge of expectation and evidence in visual activities. Evaluation of decision making criteria based on distributed message propagation in Bayesian belief networks is examined for a mechanism that brings together interactions between processing modules. On the other hand, by regarding the spatio-temporal regularities in the moving patterns of objects in the scene as a network of temporally dependent belief hypothesis, visual expectations can be represented by the most likely combinations of hypotheses by updating the network in response to instantaneous visual evidence. Such expectations in turn could be used for visual attention. In particular, we relate the concept of vision as behavior with results from some of our early studies on visual augmented hidden Markov model for representing `hidden' regularities in object motion and producing dynamic expectations of the moving object in the scene.