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@INPROCEEDINGS{Bodenstein:826187,
      author       = {Bodenstein, Christian and Götz, Markus and Jansen, Annika
                      and Scholz, Henrike and Riedel, Morris},
      title        = {{A}utomatic {O}bject {D}etection using {DBSCAN} for
                      {C}ounting {I}ntoxicated {F}lies in the {FLORIDA} {A}ssay},
      publisher    = {IEEE},
      reportid     = {FZJ-2017-00433},
      pages        = {746 - 751},
      year         = {2017},
      comment      = {ISBN 978-1-5090-6167-9},
      booktitle     = {ISBN 978-1-5090-6167-9},
      abstract     = {In this paper, we propose an instrumentation andcomputer
                      vision pipeline that allows automatic object detectionon
                      images taken from multiple experimental set ups. We
                      demon-strate the approach by autonomously counting
                      intoxicated fliesin the FLORIDA assay. The assay measures
                      the effect of ethanolexposure onto the ability of a vinegar
                      fly Drosophila melanogasterto right itself. The analysis
                      consists of a three-step approach.First, obtaining an image
                      of a large set of individual experiments,second, identify
                      areas containing a single experiment, and third,discover the
                      searched objects within the experiment. For theanalysis we
                      facilitate well-known computer vision and machinelearning
                      algorithms—namely color segmentation, threshold imag-ing
                      and DBSCAN. The automation of the experiment enables
                      anunprecedented reproducibility and consistency, while
                      significantlydecreasing the manual labor.},
      month         = {Dec},
      date          = {2016-12-18},
      organization  = {15th IEEE International Conference on
                       Machine Learning and Applications,
                       Anaheim (USA), 18 Dec 2016 - 20 Dec
                       2016},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512)},
      pid          = {G:(DE-HGF)POF3-512},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1109/ICMLA.2016.0133},
      url          = {https://juser.fz-juelich.de/record/826187},
}