1 Ocak 2017 Konferans bildirisi Açık Erişim

Ates, Hasan F.; Sunetci, Sercan

DataCite XML

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/52185</identifier>
  <creators>
    <creator>
      <creatorName>Ates, Hasan F.</creatorName>
      <givenName>Hasan F.</givenName>
      <familyName>Ates</familyName>
      <affiliation>Isik Univ, Dept Elect &amp; Elect Engn, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Sunetci, Sercan</creatorName>
      <givenName>Sercan</givenName>
      <familyName>Sunetci</familyName>
      <affiliation>Isik Univ, Dept Elect &amp; Elect Engn, Istanbul, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Improving Semantic Segmentation With Generalized Models Of Local Context</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/52185</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1007/978-3-319-64698-5_27</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">Semantic segmentation (i.e. image parsing) aims to annotate each image pixel with its corresponding semantic class label. Spatially consistent labeling of the image requires an accurate description and modeling of the local contextual information. Superpixel image parsing methods provide this consistency by carrying out labeling at the superpixel-level based on superpixel features and neighborhood information. In this paper, we develop generalized and flexible contextual models for superpixel neighborhoods in order to improve parsing accuracy. Instead of using a fixed segmentation and neighborhood definition, we explore various contextual models to combine complementary information available in alternative superpixel segmentations of the same image. Simulation results on two datasets demonstrate significant improvement in parsing accuracy over the baseline approach.</description>
  </descriptions>
</resource>