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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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    <dct:title>Software for Bubble identification from images with machine learning methods</dct:title>
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    <dcat:keyword>Bubbly flows</dcat:keyword>
    <dcat:keyword>Deep Learning</dcat:keyword>
    <dcat:keyword>Computer Vision</dcat:keyword>
    <dcat:keyword>CNN</dcat:keyword>
    <dcat:keyword>Semantic segmentation</dcat:keyword>
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    <dct:description>&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;</dct:description>
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Publication date:
August 5, 2022
DOI:
10.14278/rodare.2566

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Keyword(s):
Bubbly flows Deep Learning Computer Vision CNN Semantic segmentation
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Version 1.0 10.14278/rodare.2566 Aug 5, 2022
Version 1.0 10.14278/rodare.2329 Aug 5, 2022
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