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Using logic in a model-based approach to object recognition

A model-based object recognition system, predicated on logic as a method of modeling, describing, and identifying objects, is proposed. Users supply the object recognition system with models of known object classes in the form of production rules. The system describes each instance of an object found within an image scene as a collection of facts. The modeled rules act upon these facts in a Prolog environment to obtain an interpretation of the original image scene. Since users supply the object models and the Prolog environment supplies the inference mechanism for interpretation, the primary task of the object recognition system is the description process. For each object instance identified within the image scene, declarative statements are formulated which represent observed components, features, or attributes of that object. The description of an object instance is restricted to its geometric components which are derived from a skeleton or stick-figure representation of a 2-D silhouette portrayal of an object found in the original image scene. Therefore, object classifications can be modeled in a general way so that the size and orientation of the object is independent of that model. Sample results are presented.