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dc.contributor.authorConnell, Jonathan Hudsonen_US
dc.date.accessioned2004-10-20T20:03:37Z
dc.date.available2004-10-20T20:03:37Z
dc.date.issued1985-09-01en_US
dc.identifier.otherAITR-853en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/6870
dc.description.abstractWe 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.en_US
dc.format.extent101 p.en_US
dc.format.extent10686540 bytes
dc.format.extent4012801 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesAITR-853en_US
dc.titleLearning Shape Descriptions: Generating and Generalizing Models of Visual Objectsen_US


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