A Distributed Approach to Image Interpretation Using Model-Based Spatial Reasoning

We address the problem of finding a consistent interpretation of an image when a number of object features may be detected independently, but unreliably, and their relative positions are known to be constrained. Our method treats feature detection and the application of spatial constraints as co-operating processes. We show that a Point Distribution Model can be used to model constraints on the configuration of features and that the model parameters define a convenient configuration space in which a region representing the set of currently feasible configurations can be maintained. We also introduce the idea of dealing with spatially compact groups of feature hypotheses rather than single hypotheses. We describe two reasoning strategies for dealing with hypothesis groups and feasible configuration regions. These lead to an efficient and exact solution to combinatorially explosive image interpretation problems. We demonstrate the feasibility of the approach by showing results for a system designed to interpret lateral skull radiographs.