Generating and Generalizing Models of Visual Objects

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/5629

Full metadata record

DC Field	Value	Language
dc.creator	Connell, Jonathan H.	-
dc.creator	Brady, Michael	-
dc.date	2004-10-01T20:17:30Z	-
dc.date	2004-10-01T20:17:30Z	-
dc.date	1985-07-01	-
dc.date.accessioned	2013-10-09T02:40:27Z	-
dc.date.available	2013-10-09T02:40:27Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-823	-
dc.identifier	http://hdl.handle.net/1721.1/5629	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	We report on initial experiments with an implemented learning system whose inputs are images of two-dimensional shapes. The system first builds semantic network descriptions of shapes based on Brady's smoothed local symmetry representation. It learns shape models form them using a substantially modified version of Winston's ANALOGY program. A generalization of Gray coding enables the representation to be extended and also allows a single operation, called ablation, to achieve the effects of many standard induction heuristics. The program can learn disjunctions, and can learn concepts suing only positive examples. We discuss learnability and the pervasive importance of representational hierarchies.	-
dc.format	24 p.	-
dc.format	4899583 bytes	-
dc.format	3834482 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-823	-
dc.subject	vision	-
dc.subject	learning	-
dc.subject	shape description	-
dc.subject	representation of shape	-
dc.title	Generating and Generalizing Models of Visual Objects	-
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