  We show an approach to automated control of machine vision systems based on
incremental creation and evaluation of a particular family of influence
diagrams that represent hypotheses of imagery interpretation and possible
subsequent processing decisions. In our approach, model-based machine vision
techniques are integrated with hierarchical Bayesian inference to provide a
framework for representing and matching instances of objects and relationships
in imagery and for accruing probabilities to rank order conflicting scene
interpretations. We extend a result of Tatman and Shachter to show that the
sequence 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 diagram that contains all random variables created during
the run of the vision system.
