An Optimal Scale for Edge Detection
dc.date.accessioned | 2004-10-04T15:13:04Z | |
dc.date.accessioned | 2018-11-24T10:14:19Z | |
dc.date.available | 2004-10-04T15:13:04Z | |
dc.date.available | 2018-11-24T10:14:19Z | |
dc.date.issued | 1988-09-01 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/6499 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/6499 | |
dc.description.abstract | 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. | en_US |
dc.format.extent | 2655175 bytes | |
dc.format.extent | 1034256 bytes | |
dc.language.iso | en_US | |
dc.title | An Optimal Scale for Edge Detection | en_US |
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