Learning Shape Descriptions: Generating and Generalizing Models of Visual Objects

dc.creator	Connell, Jonathan Hudson
dc.date	2004-10-20T20:03:37Z
dc.date	2004-10-20T20:03:37Z
dc.date	1985-09-01
dc.date.accessioned	2013-10-09T02:47:24Z
dc.date.available	2013-10-09T02:47:24Z
dc.date.issued	2013-10-09
dc.identifier	AITR-853
dc.identifier	http://hdl.handle.net/1721.1/6870
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	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.
dc.format	101 p.
dc.format	10686540 bytes
dc.format	4012801 bytes
dc.format	application/postscript
dc.format	application/pdf
dc.language	en_US
dc.relation	AITR-853
dc.title	Learning Shape Descriptions: Generating and Generalizing Models of Visual Objects

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