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An Optimal Scale for Edge Detection

dc.date.accessioned2004-10-04T15:13:04Z
dc.date.accessioned2018-11-24T10:14:19Z
dc.date.available2004-10-04T15:13:04Z
dc.date.available2018-11-24T10:14:19Z
dc.date.issued1988-09-01en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/6499
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/6499
dc.description.abstractMany 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.en_US
dc.format.extent2655175 bytes
dc.format.extent1034256 bytes
dc.language.isoen_US
dc.titleAn Optimal Scale for Edge Detectionen_US


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