1 Ocak 2023 Dergi makalesi Açık Erişim

Sarigul, Mehmet

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/269806</identifier>
  <creators>
    <creator>
      <creatorName>Sarigul, Mehmet</creatorName>
      <givenName>Mehmet</givenName>
      <familyName>Sarigul</familyName>
    </creator>
  </creators>
  <titles>
    <title>A Survey On Digital Video Stabilization</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2023</publicationYear>
  <dates>
    <date dateType="Issued">2023-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/269806</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1007/s11042-023-14726-1</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">&lt;p&gt;Shakes and jitters are an eventual result of involuntary camera movements during video recording. Digital video stabilization is the elimination of these errors with smart algorithms. This process is usually performed in three steps which are camera motion estimation, motion correction, and stable video synthesis. In the literature, methods differ by the way they perform these steps. The recent success of deep learning has pioneered learning-based video stabilization approaches. This paper provides a detailed explanation of video stabilization methods by analyzing and comparing the applied approaches from past to present.&lt;/p&gt;</description>
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