Using perceptual inference networks to manage vision processes

The aim is to generate a hierarchical description of the scene using preattentive and attentive modules. The preattentive module provides evidence in terms of primitive organizations like parallelism, continuity, closure, and strands. The attentive organization integrates this preattentive evidence to hypothesize more complex organizations such as parallelograms, circles, ellipses, and ribbons. This attentive part is realized by the perceptual inference network (PIN) which is a form of Bayesian network. The output set of hypotheses of the PIN is large and redundant. A set of lines is described as a parallelogram and/or ellipse and/or circle. There is considerable ambiguity in such a description. The strategy is to use special-purpose modules to resolve the ambiguous hypotheses and to generate a comprehensive scene description. These special purpose modules tend to be computationally expensive and have limited applicability. Therefore, we want to apply them only when and where we expect the greatest amount of information gain per unit computational resource