An Optimal Scale for Edge Detection

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6499

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DC Field	Value	Language
dc.creator	Geiger, Davi	-
dc.creator	Poggio, Tomaso	-
dc.date	2004-10-04T15:13:04Z	-
dc.date	2004-10-04T15:13:04Z	-
dc.date	1988-09-01	-
dc.date.accessioned	2013-10-09T02:45:46Z	-
dc.date.available	2013-10-09T02:45:46Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1078	-
dc.identifier	http://hdl.handle.net/1721.1/6499	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Many problems in early vision are ill posed. Edge detection is a typical example. This paper applies regularization techniques to the problem of edge detection. We derive an optimal filter for edge detection with a size controlled by the regularization parameter $\\ lambda $ and compare it to the Gaussian filter. A formula relating the signal-to-noise ratio to the parameter $\\lambda $ is derived from regularization analysis for the case of small values of $\\lambda$. We also discuss the method of Generalized Cross Validation for obtaining the optimal filter scale. Finally, we use our framework to explain two perceptual phenomena: coarsely quantized images becoming recognizable by either blurring or adding noise.	-
dc.format	2655175 bytes	-
dc.format	1034256 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1078	-
dc.title	An Optimal Scale for Edge Detection	-
Appears in Collections:	MIT Items

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