A combined multi-scale/irregular algorithm for the vectorization of noisy digital contours
Identifieur interne : 001274 ( Main/Merge ); précédent : 001273; suivant : 001275A combined multi-scale/irregular algorithm for the vectorization of noisy digital contours
Auteurs : Antoine Vacavant [France] ; Tristan Roussillon [France] ; Bertrand Kerautret [France] ; Jacques-Olivier Lachaud [France]Source :
- Computer Vision and Image Understanding [ 1077-3142 ] ; 2013-04-01.
English descriptors
Abstract
This paper proposes and evaluates a new method for reconstructing a polygonal representation from arbitrary digital contours that are possibly damaged or coming from the segmentation of noisy data. The method consists in two stages. In the first stage, a multi-scale analysis of the contour is conducted so as to identify noisy or damaged parts of the contour as well as the intensity of the perturbation. All the identified scales are then merged so that the input data is covered by a set of pixels whose size is increased according to the local intensity of noise. The second stage consists in transforming this set of resized pixels into an irregular isothetic object composed of an ordered set of rectangular and axis-aligned cells. Its topology is stored as a Reeb graph, which allows an easy pruning of its unnecessary spurious edges. Every remaining connected part has the topology of a circle and a polygonal representation is independently computed for each of them. Four different geometrical algorithms, including a new one, are reviewed for the latter task. These vectorization algorithms are experimentally evaluated and the whole method is also compared to previous works on both synthetic and true digital images. For fair comparisons, when possible, several error measures between the reconstruction and the ground truth are given for the different techniques.
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DOI: 10.1016/j.cviu.2012.07.006
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BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-413289" type="indirect"><org type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno><idno type="IdUnivLorraine">[UL]100--</idno><orgName>Université de Lorraine</orgName><orgName type="acronym">UL</orgName><date type="start">2012-01-01</date><desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author><author><name sortKey="Lachaud, Jacques Olivier" sort="Lachaud, Jacques Olivier" uniqKey="Lachaud J" first="Jacques-Olivier" last="Lachaud">Jacques-Olivier Lachaud</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-79" status="VALID"><orgName>Laboratoire de Mathématiques</orgName><orgName type="acronym">LAMA</orgName><desc><address><addrLine>Université de Savoie, UFR SFA Domaine Universitaire, Bâtiment Le Chablais 73376 LE BOURGET DU LAC</addrLine><country key="FR"></country></address><ref type="url">http://www.lama.univ-savoie.fr/</ref></desc><listRelation><relation name="UMR5127" active="#struct-441569" type="direct"></relation><relation active="#struct-432896" type="direct"></relation></listRelation><tutelles><tutelle name="UMR5127" active="#struct-441569" type="direct"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle><tutelle active="#struct-432896" type="direct"><org type="institution" xml:id="struct-432896" status="VALID"><orgName>Université Savoie Mont Blanc</orgName><orgName type="acronym">USMB [Université de Savoie] [Université de Chambéry]</orgName><date type="start">1979-10-01</date><desc><address><addrLine>27, rue Marcoz - 73000 Chambéry</addrLine><country key="FR"></country></address><ref type="url">https://www.univ-smb.fr</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author></analytic><idno type="DOI">10.1016/j.cviu.2012.07.006</idno><series><title level="j">Computer Vision and Image Understanding</title><idno type="ISSN">1077-3142</idno><imprint><date type="datePub">2013-04-01</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Irregular grids</term><term>Multi-scale noise detection</term><term>Noisy object analysis</term><term>Reeb graph</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper proposes and evaluates a new method for reconstructing a polygonal representation from arbitrary digital contours that are possibly damaged or coming from the segmentation of noisy data. The method consists in two stages. In the first stage, a multi-scale analysis of the contour is conducted so as to identify noisy or damaged parts of the contour as well as the intensity of the perturbation. All the identified scales are then merged so that the input data is covered by a set of pixels whose size is increased according to the local intensity of noise. The second stage consists in transforming this set of resized pixels into an irregular isothetic object composed of an ordered set of rectangular and axis-aligned cells. Its topology is stored as a Reeb graph, which allows an easy pruning of its unnecessary spurious edges. Every remaining connected part has the topology of a circle and a polygonal representation is independently computed for each of them. Four different geometrical algorithms, including a new one, are reviewed for the latter task. These vectorization algorithms are experimentally evaluated and the whole method is also compared to previous works on both synthetic and true digital images. For fair comparisons, when possible, several error measures between the reconstruction and the ground truth are given for the different techniques.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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