Generating and Generalizing Models of Visual Objects

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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