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Learning Shape Descriptions: Generating and Generalizing Models of Visual Objects

Author(s)
Connell, Jonathan Hudson
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Abstract
We present the results of an implemented system for learning structural prototypes from grey-scale images. We show how to divide an object into 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 similarities can be embedded in the description language. Finally we exhibit a learning algorithm that forms class models from the descriptions produced and uses these models to recognize new members of the class.
Date issued
1985-09-01
URI
http://hdl.handle.net/1721.1/6870
Other identifiers
AITR-853
Series/Report no.
AITR-853

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  • AI Technical Reports (1964 - 2004)

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