Learning Shape Descriptions: Generating and Generalizing Models of Visual Objects

Abstract : We present the results of an implemented system for learning structural prototypes from gray-scale images. We show how to divide an object into subparts and how to encode the properties of these subparts and how to encode the properties of these subparts and the relations between them. We discuss the importance of hierarchy and grouping in representing objects and show how a notion of visual similarity can be embedded in the description language. Finally we exhibit a learning algorithm that forms class models from the descriptions produced and used these models to recognize new members of the class. Keywords: Learning; Concept learning; Shape description; Machine vision; High-level vision; (SLS)Smoothed Local Symmetries.

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