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Proceedings Paper

Visual behavior: modeling 'hidden' purposes in motion
Author(s): Shaogang Gong
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Paper Abstract

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.

Paper Details

Date Published: 16 December 1992
PDF: 11 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130863
Show Author Affiliations
Shaogang Gong, Queen Mary and Westfield College (United Kingdom)


Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

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