%0 Conference Paper
%A Bodenstein, Christian
%A Götz, Markus
%A Jansen, Annika
%A Scholz, Henrike
%A Riedel, Morris
%T Automatic Object Detection using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay
%I IEEE
%M FZJ-2017-00433
%P 746 - 751
%D 2017
%< ISBN 978-1-5090-6167-9 
%X 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.
%B 15th IEEE International Conference on Machine Learning and Applications
%C 18 Dec 2016 - 20 Dec 2016, Anaheim (USA)
Y2 18 Dec 2016 - 20 Dec 2016
M2 Anaheim, USA
%F PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
%9 Contribution to a conference proceedingsContribution to a book
%R 10.1109/ICMLA.2016.0133
%U https://juser.fz-juelich.de/record/826187