Surrogate data: A novel approach to object detection

Zbisław Tabor
<span title="2010-09-01">2010</span> <i title="Walter de Gruyter GmbH"> <a target="_blank" rel="noopener" href="" style="color: black;">International Journal of Applied Mathematics and Computer Science</a> </i> &nbsp;
Surrogate data: A novel approach to object detection In the present study a novel method is introduced to detect meaningful regions of a gray-level noisy images of binary structures. The method consists in generating surrogate data for an analyzed image. A surrogate image has the same (or almost the same) power spectrum and histogram of gray-level values as the original one but is random otherwise. Then minmax paths are generated in the original image, each characterized by its length, minmax
more &raquo; ... tensity and the intensity of the starting point. If the probability of the existence of a path with the same characteristics but within surrogate images is lower than some user-specified threshold, it is concluded that the path in the original image passes through a meaningful object. The performance of the method is tested on images corrupted by noise with varying intensity.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.2478/v10006-010-0040-4</a> <a target="_blank" rel="external noopener" href="">fatcat:2qhdiezykvdztkft5j5xzd5wsq</a> </span>
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