Bayesian Networks-Based Defects Classes Discrimination in Weld Radiographic Images
Identifieur interne : 001205 ( Hal/Corpus ); précédent : 001204; suivant : 001206Bayesian Networks-Based Defects Classes Discrimination in Weld Radiographic Images
Auteurs : Aicha-Baya Goumeidane ; Abdessalem Bouzaieni ; Nafaa Nacereddine ; Salvatore TabboneSource :
English descriptors
Abstract
Bayesian (also called Belief) Networks (BN) is a powerful knowledge representation and reasoning mechanism. Based onprobability theory involving a graphical structure and random variables, BN is widely used for classification tasks and in thispaper, BN is used as a class discrimination tool for a set of weld defects radiographic images using suitable attributes based oninvariant geometric descriptors. Tests are performed on a database of few hundred elements where the results are outstanding and verypromising, since they outperform those given by powerful SVM classifiers.
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DOI: 10.1007/978-3-319-23117-4_48
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Hal:hal-01254925Le document en format XML
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<front><div type="abstract" xml:lang="en">Bayesian (also called Belief) Networks (BN) is a powerful knowledge representation and reasoning mechanism. Based onprobability theory involving a graphical structure and random variables, BN is widely used for classification tasks and in thispaper, BN is used as a class discrimination tool for a set of weld defects radiographic images using suitable attributes based oninvariant geometric descriptors. Tests are performed on a database of few hundred elements where the results are outstanding and verypromising, since they outperform those given by powerful SVM classifiers.</div>
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<date type="whenProduced">2015-09-02</date>
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<idno type="halRefHtml">Computer Analysis of Images and Patterns (CAIP)- 16th International Conference , Sep 2015, Valetta, Malta. 9257, Lecture Notes in Computer Science. <10.1007/978-3-319-23117-4_48></idno>
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<seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno>
<idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno>
<idno type="stamp" n="LORIA2">Publications du LORIA</idno>
<idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno>
<idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno>
<idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Bayesian Networks-Based Defects Classes Discrimination in Weld Radiographic Images</title>
<author role="aut"><persName><forename type="first">Aicha-Baya</forename>
<surname>Goumeidane</surname>
</persName>
<idno type="halAuthorId">1271296</idno>
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<author role="aut"><persName><forename type="first">Abdessalem</forename>
<surname>Bouzaieni</surname>
</persName>
<idno type="halAuthorId">1271297</idno>
<affiliation ref="#struct-206044"></affiliation>
</author>
<author role="aut"><persName><forename type="first">Nafaa</forename>
<surname>Nacereddine</surname>
</persName>
<email>nafaa.nacereddine@loria.fr</email>
<idno type="halAuthorId">446135</idno>
<affiliation ref="#struct-230145"></affiliation>
</author>
<author role="aut"><persName><forename type="first">Salvatore</forename>
<surname>Tabbone</surname>
</persName>
<email>Antoine.Tabbone@loria.fr</email>
<idno type="halAuthorId">231629</idno>
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<monogr><meeting><title>Computer Analysis of Images and Patterns (CAIP)- 16th International Conference </title>
<date type="start">2015-09-02</date>
<date type="end">2015-09-04</date>
<settlement>Valetta</settlement>
<country key="MT">Malta</country>
</meeting>
<imprint><biblScope unit="serie">Lecture Notes in Computer Science</biblScope>
<biblScope unit="volume">9257</biblScope>
</imprint>
</monogr>
<idno type="doi">10.1007/978-3-319-23117-4_48</idno>
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<profileDesc><langUsage><language ident="en">English</language>
</langUsage>
<textClass><keywords scheme="author"><term xml:lang="en">Bayesian Networks</term>
<term xml:lang="en"> Weld defects</term>
<term xml:lang="en"> Geometric descriptors</term>
<term xml:lang="en"> Radiography</term>
</keywords>
<classCode scheme="halDomain" n="info.info-cv">Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]</classCode>
<classCode scheme="halTypology" n="COMM">Conference papers</classCode>
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<abstract xml:lang="en">Bayesian (also called Belief) Networks (BN) is a powerful knowledge representation and reasoning mechanism. Based onprobability theory involving a graphical structure and random variables, BN is widely used for classification tasks and in thispaper, BN is used as a class discrimination tool for a set of weld defects radiographic images using suitable attributes based oninvariant geometric descriptors. Tests are performed on a database of few hundred elements where the results are outstanding and verypromising, since they outperform those given by powerful SVM classifiers.</abstract>
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