1 Ocak 2017 Konferans bildirisi Açık Erişim

Selver, M. Alper; Zoral, E. Yesim; Belenlioglu, Burak; Dogan, Sinan

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/44529</identifier>
  <creators>
    <creator>
      <creatorName>Selver, M. Alper</creatorName>
      <givenName>M. Alper</givenName>
      <familyName>Selver</familyName>
      <affiliation>Dokuz Eylul Univ, Elect &amp; Elect Engn Dept, Izmir, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Zoral, E. Yesim</creatorName>
      <givenName>E. Yesim</givenName>
      <familyName>Zoral</familyName>
      <affiliation>Dokuz Eylul Univ, Elect &amp; Elect Engn Dept, Izmir, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Belenlioglu, Burak</creatorName>
      <givenName>Burak</givenName>
      <familyName>Belenlioglu</familyName>
      <affiliation>Res &amp; Dev Dept, Izmir, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Dogan, Sinan</creatorName>
      <givenName>Sinan</givenName>
      <familyName>Dogan</familyName>
      <affiliation>Res &amp; Dev Dept, Izmir, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Predictive Modeling For Monocular Vision Based Rail Track Extraction</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2017</publicationYear>
  <dates>
    <date dateType="Issued">2017-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/44529</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.81043/aperta.44528</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.81043/aperta.44529</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">Camera based approaches are the most widely analyzed applications of track extraction for railway systems. Finalizing the extraction with an analytic representation of the rail tracks is very useful both for representing the smooth characteristics of the railways well and for compensating the faulty image processing results. Moreover, polynomial or geometric definition of the rails creates parametric representations, which can be transformed to generate a dynamic ROI around the rails and can be used for applications like obstacle detection. However, the existing studies only use presumed knowledge and assumptions about the geometry of railway tracks. This paper provides a statistical analysis of railway track turns on various video records to quantify the margins of variations. Moreover, based on the results of those statistical analyses, this study introduces a method to predict the railway tracks by means of polynomial approximation followed by multilayer perceptron networks.</description>
  </descriptions>
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