Unsupervised Smooth Contour Detection
Rafael Grompone von Gioi, Gregory Randall
→ BibTeX
@article{ipol.2016.175,
    title   = {{Unsupervised Smooth Contour Detection}},
    author  = {Grompone von Gioi, Rafael and Randall, Gregory},
    journal = {{Image Processing On Line}},
    volume  = {6},
    pages   = {233--267},
    year    = {2016},
    doi     = {10.5201/ipol.2016.175},
}
% if your bibliography style doesn't support doi fields:
    note    = {\url{https://doi.org/10.5201/ipol.2016.175}}
published
2016-11-18
reference
Rafael Grompone von Gioi, and Gregory Randall, Unsupervised Smooth Contour Detection, Image Processing On Line, 6 (2016), pp. 233–267. https://doi.org/10.5201/ipol.2016.175

Communicated by José Lezama
Demo edited by Rafael Grompone

Abstract

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.

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