Robust and accurate vectorization of line drawings
Identifieur interne : 005590 ( Main/Exploration ); précédent : 005589; suivant : 005591Robust and accurate vectorization of line drawings
Auteurs : Xavier Hilaire [France] ; Karl Tombre [France]Source :
- IEEE transactions on pattern analysis and machine intelligence [ 0162-8828 ] ; 2006.
Descripteurs français
- KwdFr :
- Algorithmes, Amélioration d'image (), Analyse numérique assistée par ordinateur, Infographie, Intelligence artificielle, Interprétation d'image assistée par ordinateur (), Mémorisation et recherche des informations (), Reconnaissance automatique des formes (), Sensibilité et spécificité, Traitement du signal assisté par ordinateur.
- Pascal (Inist)
- Algorithmes, Amélioration d'image, Analyse numérique assistée par ordinateur, Infographie, Intelligence artificielle, Interprétation d'image assistée par ordinateur, Mémorisation et recherche des informations, Reconnaissance automatique des formes, Segmentation image, Analyse forme, Graphisme, Interprétation image, Analyse documentaire, Image binaire, Sensibilité et spécificité, Traitement du signal assisté par ordinateur, Vectorisation, Reconnaissance graphique, Interprétation graphique.
English descriptors
- KwdEn :
- Algorithms, Artificial Intelligence, Binary image, Computer Graphics, Document analysis, Graphical recognition, Graphism, Image Enhancement (methods), Image Interpretation, Computer-Assisted (methods), Image interpretation, Image segmentation, Information Storage and Retrieval (methods), Numerical Analysis, Computer-Assisted, Pattern Recognition, Automated (methods), Pattern analysis, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Vectorization.
- MESH :
- mix :
Abstract
This paper presents a method for vectorizing the graphical parts of paper-based line drawings. The method consists of separating the input binary image into layers of homogeneous thickness, skeletonizing each layer, segmenting the skeleton by a method based on random sampling, and simplifying the result. The segmentation method is robust with a best bound of 50 percent noise reached for indefinitely long primitives. Accurate estimation of the recognized vector's parameters is enabled by explicitly computing their feasibility domains. Theoretical performance analysis and expression of the complexity of the segmentation method are derived. Experimental results and comparisons with other vectorization systems are also provided.
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Le document en format XML
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lès-Nancy</settlement></placeName></affiliation></author><author><name sortKey="Tombre, Karl" sort="Tombre, Karl" uniqKey="Tombre K" first="Karl" last="Tombre">Karl Tombre</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>LORIA, 615 rue du Jardin Botanique</s1><s2>54602 Villers- lès-Nancy</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Villers- lès-Nancy</settlement></placeName></affiliation></author></analytic><series><title level="j" type="main">IEEE transactions on pattern analysis and machine intelligence</title><title level="j" type="abbreviated">IEEE trans. pattern anal. mach. intell.</title><idno type="ISSN">0162-8828</idno><imprint><date when="2006">2006</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">IEEE transactions on pattern analysis and machine intelligence</title><title level="j" type="abbreviated">IEEE trans. pattern anal. mach. intell.</title><idno type="ISSN">0162-8828</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Artificial Intelligence</term><term>Binary image</term><term>Computer Graphics</term><term>Document analysis</term><term>Graphical recognition</term><term>Graphism</term><term>Image Enhancement (methods)</term><term>Image Interpretation, Computer-Assisted (methods)</term><term>Image interpretation</term><term>Image segmentation</term><term>Information Storage and Retrieval (methods)</term><term>Numerical Analysis, Computer-Assisted</term><term>Pattern Recognition, Automated (methods)</term><term>Pattern analysis</term><term>Sensitivity and Specificity</term><term>Signal Processing, Computer-Assisted</term><term>Vectorization</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Amélioration d'image ()</term><term>Analyse numérique assistée par ordinateur</term><term>Infographie</term><term>Intelligence artificielle</term><term>Interprétation d'image assistée par ordinateur ()</term><term>Mémorisation et recherche des informations ()</term><term>Reconnaissance automatique des formes ()</term><term>Sensibilité et spécificité</term><term>Traitement du signal assisté par ordinateur</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Image Enhancement</term><term>Image Interpretation, Computer-Assisted</term><term>Information Storage and Retrieval</term><term>Pattern Recognition, Automated</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Artificial Intelligence</term><term>Computer Graphics</term><term>Numerical Analysis, Computer-Assisted</term><term>Sensitivity and Specificity</term><term>Signal Processing, Computer-Assisted</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Algorithmes</term><term>Amélioration d'image</term><term>Analyse numérique assistée par ordinateur</term><term>Infographie</term><term>Intelligence artificielle</term><term>Interprétation d'image assistée par ordinateur</term><term>Mémorisation et recherche des informations</term><term>Reconnaissance automatique des formes</term><term>Segmentation image</term><term>Analyse forme</term><term>Graphisme</term><term>Interprétation image</term><term>Analyse documentaire</term><term>Image binaire</term><term>Sensibilité et spécificité</term><term>Traitement du signal assisté par ordinateur</term><term>Vectorisation</term><term>Reconnaissance graphique</term><term>Interprétation graphique</term></keywords><keywords scheme="mix" xml:lang="en"><term>curve segmentation</term><term>document analysis</term><term>graphics recognition and interpretation</term><term>line drawings</term><term>performance evaluation</term><term>vectorization</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper presents a method for vectorizing the graphical parts of paper-based line drawings. The method consists of separating the input binary image into layers of homogeneous thickness, skeletonizing each layer, segmenting the skeleton by a method based on random sampling, and simplifying the result. The segmentation method is robust with a best bound of 50 percent noise reached for indefinitely long primitives. Accurate estimation of the recognized vector's parameters is enabled by explicitly computing their feasibility domains. Theoretical performance analysis and expression of the complexity of the segmentation method are derived. Experimental results and comparisons with other vectorization systems are also provided.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Villers- lès-Nancy</li></settlement></list><tree><country name="France"><region name="Grand Est"><name sortKey="Hilaire, Xavier" sort="Hilaire, Xavier" uniqKey="Hilaire X" first="Xavier" last="Hilaire">Xavier Hilaire</name></region><name sortKey="Tombre, Karl" sort="Tombre, Karl" uniqKey="Tombre K" first="Karl" last="Tombre">Karl Tombre</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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