Schema labelling is a representation theory which focuses on composition and specialization as two major aspects of machine perception. Previous research in computer vision and knowledge representation have identified computational mechanisms for these tasks. We show that the representational adequacy of schema knowledge structures can be combined advantageously with the constraint propagation capabilities of network consistency techniques. In particular, composition and specialization can be realized as mutually- interdependent cooperative processes which operate on the same underlying knowledge representation. In this theory, a schema is a generative representation for a class of semantically related objects. Composition builds a structural description of the scene from rules defined in each schema. The scene description is represented as a network consistency graph which makes explicit the objects found in the scene and their semantic relationships. The graph is hierarchical and describes the input scene at varying levels of detail. Specialization applies network consistency techniques to refine the graph towards a global scene description. Schema labelling is being used for interpreting hand-printed Chinese characters , and for recognizing VLSI circuit designs from their mask layouts .
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