Unsupervised Smooth Contour Detection
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...
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Image Processing On Line
2016-11-01
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Online Access: | http://www.ipol.im/pub/art/2016/175/ |
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doaj-f5d02e48ddd549519d2e836e7148ea552020-11-24T22:46:20ZengImage Processing On LineImage Processing On Line2105-12322016-11-01623326710.5201/ipol.2016.175Unsupervised Smooth Contour DetectionRafael Grompone von GioiGregory RandallAn 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.http://www.ipol.im/pub/art/2016/175/ |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rafael Grompone von Gioi Gregory Randall |
spellingShingle |
Rafael Grompone von Gioi Gregory Randall Unsupervised Smooth Contour Detection Image Processing On Line |
author_facet |
Rafael Grompone von Gioi Gregory Randall |
author_sort |
Rafael Grompone von Gioi |
title |
Unsupervised Smooth Contour Detection |
title_short |
Unsupervised Smooth Contour Detection |
title_full |
Unsupervised Smooth Contour Detection |
title_fullStr |
Unsupervised Smooth Contour Detection |
title_full_unstemmed |
Unsupervised Smooth Contour Detection |
title_sort |
unsupervised smooth contour detection |
publisher |
Image Processing On Line |
series |
Image Processing On Line |
issn |
2105-1232 |
publishDate |
2016-11-01 |
description |
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. |
url |
http://www.ipol.im/pub/art/2016/175/ |
work_keys_str_mv |
AT rafaelgromponevongioi unsupervisedsmoothcontourdetection AT gregoryrandall unsupervisedsmoothcontourdetection |
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1725685196795150336 |