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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},
}