An Analysis of the Effect of Gaussian Error in Object Recognition

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7057

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DC Field	Value	Language
dc.creator	Sarachik, Karen Beth	-
dc.date	2004-10-20T20:24:12Z	-
dc.date	2004-10-20T20:24:12Z	-
dc.date	1994-02-01	-
dc.date.accessioned	2013-10-09T02:48:07Z	-
dc.date.available	2013-10-09T02:48:07Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AITR-1469	-
dc.identifier	http://hdl.handle.net/1721.1/7057	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Object recognition is complicated by clutter, occlusion, and sensor error. Since pose hypotheses are based on image feature locations, these effects can lead to false negatives and positives. In a typical recognition algorithm, pose hypotheses are tested against the image, and a score is assigned to each hypothesis. We use a statistical model to determine the score distribution associated with correct and incorrect pose hypotheses, and use binary hypothesis testing techniques to distinguish between them. Using this approach we can compare algorithms and noise models, and automatically choose values for internal system thresholds to minimize the probability of making a mistake.	-
dc.format	7376380 bytes	-
dc.format	3521585 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AITR-1469	-
dc.title	An Analysis of the Effect of Gaussian Error in Object Recognition	-
Appears in Collections:	MIT Items

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