Interpretive Vision and Restriction Graphs

Abstract

We describe an approach to image interpretation which uses a dynamically determined interaction of prediction and observation. We provide a representational mechansim, built on our geometric modeling scheme which facilitates the computational processes necessary for image interpretation. The mechanism implements generic object classes and specializations of models, enables case analysis in reasoning about incompletely specified situations, and manages multiple hypothesized instantiations of modeled objects in a single image. It is based on restriction nodes and quantified variables. A natural partial order on restriction nodes can be defined by comparing the satisifiability of their constraints. Nodes are arranged in an incomplete restriction graph whose arcs represent relations of nodes under the partial order. Predictions are matched to descriptions by finding maximal isomorphic subgraphs of a prediction graph and an observation graph [8] subject to a naturally associated infimum of restriction nodes being satisifiable. In this manner constraints implied by local two dimensional matches of image features to predicted features are propagated back to the three dimensional model enforcing global consistency.