Machine Recognition as Representation and Search

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6003

Full metadata record

DC Field	Value	Language
dc.creator	Zhao, Feng	-
dc.date	2004-10-04T14:35:37Z	-
dc.date	2004-10-04T14:35:37Z	-
dc.date	1989-12-01	-
dc.date.accessioned	2013-10-09T02:42:23Z	-
dc.date.available	2013-10-09T02:42:23Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1189	-
dc.identifier	http://hdl.handle.net/1721.1/6003	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Generality, representation, and control have been the central issues in machine recognition. Model-based recognition is the search for consistent matches of the model and image features. We present a comparative framework for the evaluation of different approaches, particularly those of ACRONYM, RAF, and Ikeuchi et al. The strengths and weaknesses of these approaches are discussed and compared and the remedies are suggested. Various tradeoffs made in the implementations are analyzed with respect to the systems' intended task-domains. The requirements for a versatile recognition system are motivated. Several directions for future research are pointed out.	-
dc.format	40 p.	-
dc.format	6338900 bytes	-
dc.format	2496576 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1189	-
dc.subject	computer vision	-
dc.subject	representation	-
dc.subject	search control	-
dc.subject	objectsmodeling	-
dc.subject	consistent labeling	-
dc.subject	model-based recognition	-
dc.title	Machine Recognition as Representation and Search	-
Appears in Collections:	MIT Items

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