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{ "extrinsic": { "provider": "https://deposit.softwareheritage.org/1/private/1286/meta/", "raw": { "origin": { "type": "deposit", "url": "https://doi.org/10.5201/ipol.2016.175" }, "origin_metadata": { "metadata": { "atom:external_identifier": "ipol.2016.175", "atom:title": "ipol_175", "codemeta:applicationCategory": "Image Processing", "codemeta:author": [ { "codemeta:affiliation": "CMLA, ENS Cachan, France", "codemeta:name": "Rafael Grompone von Gioi" }, { "codemeta:affiliation": "IIE, Universidad de la Rep\u00b4ublica, Uruguay", "codemeta:name": "Gregory Randall" } ], "codemeta:dateCreated": "2016-08-26", "codemeta:datePublished": "2016-11-18", "codemeta:description": "Unsupervised Smooth Contour Detection", "codemeta:downloadUrl": "http://www.ipol.im/pub/art/2016/175/smooth_contours_1.0.zip", "codemeta:identifier": "https://doi.org/10.5201/ipol.2016.175", "codemeta:isPartOf": { "codemeta:identifier": "ISSN: 2105-1232 DOI: 10.5201/ipol", "codemeta:name": "Image Processing On Line (IPOL)", "codemeta:type": "Journal" }, "codemeta:keywords": [ "contour detection", "unsupervised", "sub-pixel accuracy", "a contrario", "NFA", "Mann-" ], "codemeta:license": { "codemeta:name": "AGPL-3.0-or-later", "codemeta:url": "https://spdx.org/licenses/AGPL-3.0-or-later.html" }, "codemeta:operatingSystem": "Linux", "codemeta:programmingLanguage": "C", "codemeta:referencePublication": { "codemeta: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.", "codemeta:identifier": "https://doi.org/10.5201/ipol.2016.175", "codemeta:name": "Unsupervised Smooth Contour Detection", "codemeta:url": "http://www.ipol.im/pub/art/2016/175/article.pdf" }, "codemeta:relatedLink": "http://demo.ipol.im/demo/175", "codemeta:releaseNotes": "This code implements the algorithm(s) published in the IPOL paper \"Unsupervised Smooth Contour Detection\"", "codemeta:url": "http://www.ipol.im/pub/art/2016/175/", "codemeta:version": "1.0" }, "provider": { "metadata": {}, "provider_name": "", "provider_type": "deposit_client", "provider_url": "https://doi.org/10.5201/" }, "tool": { "configuration": { "sword_version": "2" }, "name": "swh-deposit", "version": "0.8.0" } } }, "when": "2020-12-20T14:36:44.367321+00:00" }, "original_artifact": [ { "checksums": { "sha1": "98227057b55fc6cc733613ffaba8e663670214bf", "sha256": "67b9bb43012cd35563062084705f0f6e6c68075ce64e8f2383572f5cb709c9e5" }, "filename": "archive.zip", "length": 765358, "url": "https://deposit.softwareheritage.org/1/private/1286/raw/" } ] }
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ipol: Deposit 1286 in collection ipol
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smooth_contours_1.0 |
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