Limitations of Geometric Hashing in the Presence of Gaussian Noise

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

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