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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">109</journal-id>
      <journal-id journal-id-type="index">urn:lsid:arphahub.com:pub:3dc5f44e-8666-58db-bc76-a455210e8891</journal-id>
      <journal-title-group>
        <journal-title xml:lang="en">JUCS - Journal of Universal Computer Science</journal-title>
        <abbrev-journal-title xml:lang="en">jucs</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">0948-695X</issn>
      <issn pub-type="epub">0948-6968</issn>
      <publisher>
        <publisher-name>Journal of Universal Computer Science</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.3217/jucs-015-04-0786</article-id>
      <article-id pub-id-type="publisher-id">29341</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group subj-group-type="scientific_subject">
          <subject>I.4.0 - General</subject>
          <subject>I.4.6 - Segmentation</subject>
          <subject>I.4.8 - Scene Analysis</subject>
          <subject>I.4.9 - Applications</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Graph-based Approach for Robust Road Guidance Sign Recognition from Differently Exposed Images</article-title>
      </title-group>
      <contrib-group content-type="authors">
        <contrib contrib-type="author" corresp="yes">
          <name name-style="western">
            <surname>Vavilin</surname>
            <given-names>Andrey</given-names>
          </name>
          <email xlink:type="simple">andy@islab.ulsan.ac.kr</email>
          <xref ref-type="aff" rid="A1">1</xref>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Jo</surname>
            <given-names>Kang-Hyun</given-names>
          </name>
          <xref ref-type="aff" rid="A1">1</xref>
        </contrib>
      </contrib-group>
      <aff id="A1">
        <label>1</label>
        <addr-line content-type="verbatim">University of Ulsan, Ulsan, Republic of Korea</addr-line>
        <institution>University of Ulsan</institution>
        <addr-line content-type="city">Ulsan</addr-line>
        <country>Republic of Korea</country>
      </aff>
      <author-notes>
        <fn fn-type="corresp">
          <p>Corresponding author: Andrey Vavilin (<email xlink:type="simple">andy@islab.ulsan.ac.kr</email>).</p>
        </fn>
        <fn fn-type="edited-by">
          <p>Academic editor: </p>
        </fn>
      </author-notes>
      <pub-date pub-type="collection">
        <year>2009</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>28</day>
        <month>02</month>
        <year>2009</year>
      </pub-date>
      <volume>15</volume>
      <issue>4</issue>
      <fpage>786</fpage>
      <lpage>804</lpage>
      <uri content-type="arpha" xlink:href="http://openbiodiv.net/F02146CB-869B-5202-A1F0-53B7AC05C1C5">F02146CB-869B-5202-A1F0-53B7AC05C1C5</uri>
      <uri content-type="zenodo_dep_id" xlink:href="https://zenodo.org/record/7000693">7000693</uri>
      <permissions>
        <copyright-statement>Andrey Vavilin, Kang-Hyun Jo</copyright-statement>
        <license license-type="creative-commons-attribution" xlink:href="" xlink:type="simple">
          <license-p>This article is freely available under the J.UCS Open Content License.</license-p>
        </license>
      </permissions>
      <abstract>
        <label>Abstract</label>
        <p>In this paper we present an approach to detect traffic guidance signs and recognise the structure of junction information on them. The detection algorithm is based on using differently exposed images. These images are combined into one using tone mapping technique in order to minimize effects of bad environment conditions and low dynamic range of CCD-cameras. This technique allows robust sign detection in various lighting conditions. To localize sign candidates color segmentation is used. To minimize number of false detection filtering operations based on geometrical and color properties is applied. Recognition process is based on graph theory. Each sign candidate is decomposed into principal components and the region which represents junction structure is mapped into a graph. This graph is checked for possible mapping mistakes. Finally, the graph is analyzed in order to extract all possible paths of junction crossing. These paths must represent the real structure of the junction and correspond to the road law. The proposed method allows more effective detection in different lighting and environmental conditions such as insufficient or excessive lighting, rain, fog etc compared with conventional approaches.</p>
      </abstract>
    </article-meta>
  </front>
</article>
