[IPOL announce] new article: Unsupervised Smooth Contour Detection

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2016/175/

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

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.