Limitations of Geometric Hashing in the Presence of Gaussian Noise

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/5956

Full metadata record

DC Field	Value	Language
dc.creator	Sarachik, Karen B.	-
dc.date	2004-10-04T14:16:03Z	-
dc.date	2004-10-04T14:16:03Z	-
dc.date	1992-10-01	-
dc.date.accessioned	2013-10-09T02:42:05Z	-
dc.date.available	2013-10-09T02:42:05Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1395	-
dc.identifier	http://hdl.handle.net/1721.1/5956	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	This paper presents a detailed error analysis of geometric hashing for 2D object recogition. We analytically derive the probability of false positives and negatives as a function of the number of model and image, features and occlusion, using a 2D Gaussian noise model. The results are presented in the form of ROC (receiver-operating characteristic) curves, which demonstrate that the 2D Gaussian error model always has better performance than that of the bounded uniform model. They also directly indicate the optimal performance that can be achieved for a given clutter and occlusion rate, and how to choose the thresholds to achieve these rates.	-
dc.format	15 p.	-
dc.format	207191 bytes	-
dc.format	582417 bytes	-
dc.format	application/octet-stream	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1395	-
dc.subject	object recognition	-
dc.subject	error analysis	-
dc.subject	geometric hashing	-
dc.subject	sGaussian error models	-
dc.title	Limitations of Geometric Hashing in the Presence of Gaussian Noise	-
Appears in Collections:	MIT Items

Files in This Item:

There are no files associated with this item.

DSpace JSPUI