Model-Based Influence Diagrams for Machine Vision

We show the soundness of automated con­ trol of machine vision systems based on in­ cremental creation and evaluation of a par­ ticular family of influence diagrams that rep­ resent hypotheses of imagery interpretation and possible subsequent processing decisions. In our approach, model-based machine vi­ sion techniques are integrated with hierarchi­ cal Bayesian inference to provide a framework for representing and matching instances of ob­ jects and relationships in imagery, and for ac­ cruing probabilities to rank order con:liicting scene interpretations. We extend a result of Ta tman and Shachter to show that the se­ quence of processing decisions derived from evaluating the diagrams at each stage is the same as the sequence that would have been derived by evaluating the final influence dia­ gram that contains all random variables cre­ ated during the run of the vision system.