Using Perceptual Inference Networks to Manage Vision Processes

Abstract

We provide a probabilistic framework, based on Perceptual Inference Networks, for the management of computational resources such as special purpose modules, feature detectors, and highly domain dependent algorithms. Since these resources tend to be computationally expensive and have limited applicability, judicious management is warranted. The resources are used to build a comprehensive description of the scene. Resources are selected in an information theoretic framework with the maximization of information gain per unit of computation as the optimality criterion. The viability of the algorithm is demonstrated in perceptual organization tasks.