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Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12008/8903 How cite
Title: Unsupervised smooth contour detection
Authors: Grompone von Gioi, Rafael
Randall, Gregory
Type: Artículo
Keywords: Unsupervised, Contour detection, Sub-pixel accuracy, NFA, Mann- Whitney U test, Multiple hypothesis testing
Issue Date: 2016
Abstract: An unsupervised method for detecting smooth contours in digital images is proposed. Following the a contrario approach, the starting point is dening 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 signicantly larger values than the other. Signicance is evalu-ted 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.
IN: IPOL. Journal Image Processing On Line, vol.6, pp. 233–267
ISSN: 2105-1232
Citation: Grompone von Gio, Rafael, Randall, Gregory. "Unsupervised smooth contour detection". IPOL. Journal Image Processing On Line. [en línea] 2016, vol. 6, pp. 233-267.
License: Licencia Creative Commons Atribución – No Comercial – Sin Derivadas (CC BY-NC-SA 4.0)
Appears in Collections:Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica

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