The Combinatorics of Heuristic Search Termination for Object Recognition in Cluttered Environments

dc.creator	Grimson, W. Eric L.
dc.date	2004-10-04T14:36:16Z
dc.date	2004-10-04T14:36:16Z
dc.date	1989-05-01
dc.date.accessioned	2013-10-09T02:42:27Z
dc.date.available	2013-10-09T02:42:27Z
dc.date.issued	2013-10-09
dc.identifier	AIM-1111
dc.identifier	http://hdl.handle.net/1721.1/6027
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	Many recognition systems use constrained search to locate objects in cluttered environments. Earlier analysis showed that the expected search is quadratic in the number of model and data features, if all the data comes from one object, but is exponential when spurious data is included. To overcome this, many methods terminate search once an interpretation that is "good enough" is found. We formally examine the combinatorics of this, showing that correct termination procedures dramatically reduce search. We provide conditions on the object model and the scene clutter such that the expected search is quartic. These results are shown to agree with empirical data for cluttered object recognition.
dc.format	30 p.
dc.format	3882593 bytes
dc.format	1505756 bytes
dc.format	application/postscript
dc.format	application/pdf
dc.language	en_US
dc.relation	AIM-1111
dc.subject	computer vision
dc.subject	object recognition
dc.subject	search
dc.subject	combinatorics
dc.title	The Combinatorics of Heuristic Search Termination for Object Recognition in Cluttered Environments

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