Machine Recognition as Representation and Search

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

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