文章基本信息

标题：Unsupervised Smooth Contour Detection
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作者：Rafael Grompone von Gioi ; Gregory Randall
期刊名称：Image Processing On Line
电子版ISSN：2105-1232
出版年度：2016
卷号：6
页码：233-267
DOI：10.5201/ipol.2016.175
出版社：Image Processing On Line
摘要：An unsupervised method for detecting smooth contours in digital images is proposed. Following the a contrario approach, the starting point is defining the conditions where contours should not be detected: soft gradient regions contaminated by noise. To achieve this, low frequencies are removed from the input image. Then, contours are validated as the frontiers separating two adjacent regions, one with significantly larger values than the other. Significance is evaluated using the Mann-Whitney U test to determine whether the samples were drawn from the same distribution or not. This test makes no assumption on the distributions. The resulting algorithm is similar to the classic Marr-Hildreth edge detector, with the addition of the statistical validation step. Combined with heuristics based on the Canny and Devernay methods, an efficient algorithm is derived producing sub-pixel contours.