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Software for Bubble identification from images with machine learning methods

Hessenkemper, Hendrik; Starke, Sebastian; Atassi, Yazan; Ziegenhein, Thomas; Lucas, Dirk


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  <identifier identifierType="DOI">10.14278/rodare.2566</identifier>
  <creators>
    <creator>
      <creatorName>Hessenkemper, Hendrik</creatorName>
      <givenName>Hendrik</givenName>
      <familyName>Hessenkemper</familyName>
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      <affiliation>HZDR</affiliation>
    </creator>
    <creator>
      <creatorName>Starke, Sebastian</creatorName>
      <givenName>Sebastian</givenName>
      <familyName>Starke</familyName>
      <affiliation>HZDR</affiliation>
    </creator>
    <creator>
      <creatorName>Atassi, Yazan</creatorName>
      <givenName>Yazan</givenName>
      <familyName>Atassi</familyName>
      <affiliation>HZDR</affiliation>
    </creator>
    <creator>
      <creatorName>Ziegenhein, Thomas</creatorName>
      <givenName>Thomas</givenName>
      <familyName>Ziegenhein</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-1378-3900</nameIdentifier>
      <affiliation>TIVConsultancy</affiliation>
    </creator>
    <creator>
      <creatorName>Lucas, Dirk</creatorName>
      <givenName>Dirk</givenName>
      <familyName>Lucas</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0463-2278</nameIdentifier>
      <affiliation>HZDR</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Software for Bubble identification from images with machine learning methods</title>
  </titles>
  <publisher>Rodare</publisher>
  <publicationYear>2022</publicationYear>
  <subjects>
    <subject>Bubbly flows</subject>
    <subject>Deep Learning</subject>
    <subject>Computer Vision</subject>
    <subject>CNN</subject>
    <subject>Semantic segmentation</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2022-08-05</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/2566</alternateIdentifier>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.14278/rodare.1470</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/energy</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/fwd</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/hzdr</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/rodare</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This package contains the software and the trained models described in the publication &lt;a href="https://arxiv.org/abs/2202.03107"&gt;&amp;quot;Bubble identification from images with machine learning methods&amp;quot;&lt;/a&gt;. Please refer to the README.md for installation instructions and to the Prediction_demo.ipynb for usage demonstration.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Update&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The Prediction_demo.ipynb includes now an example how to prepare the predictions for the tracking algorithm of &lt;a href="https://arxiv.org/abs/2306.02101"&gt;&amp;quot;Fate of bubble clusters rising in a quiescent liquid&amp;quot;&lt;/a&gt;. The tracking code can be found &lt;a href="https://rodare.hzdr.de/record/2316"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Update&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Now only tensorflow models are used, so no MXNet installation required.&lt;/p&gt;</description>
  </descriptions>
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