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000826187 1001_ $$0P:(DE-Juel1)164357$$aBodenstein, Christian$$b0$$eCorresponding author
000826187 1112_ $$a15th IEEE International Conference on Machine Learning and Applications$$cAnaheim$$d2016-12-18 - 2016-12-20$$gIEEE ICMLA'16$$wUSA
000826187 245__ $$aAutomatic Object Detection using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay
000826187 260__ $$bIEEE$$c2017
000826187 29510 $$aISBN 978-1-5090-6167-9 
000826187 300__ $$a746 - 751
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000826187 520__ $$aIn this paper, we propose an instrumentation andcomputer vision pipeline that allows automatic object detectionon images taken from multiple experimental set ups. We demon-strate the approach by autonomously counting intoxicated fliesin the FLORIDA assay. The assay measures the effect of ethanolexposure onto the ability of a vinegar fly Drosophila melanogasterto right itself. The analysis consists of a three-step approach.First, obtaining an image of a large set of individual experiments,second, identify areas containing a single experiment, and third,discover the searched objects within the experiment. For theanalysis we facilitate well-known computer vision and machinelearning algorithms—namely color segmentation, threshold imag-ing and DBSCAN. The automation of the experiment enables anunprecedented reproducibility and consistency, while significantlydecreasing the manual labor.
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000826187 7001_ $$0P:(DE-Juel1)162390$$aGötz, Markus$$b1
000826187 7001_ $$0P:(DE-HGF)0$$aJansen, Annika$$b2
000826187 7001_ $$0P:(DE-HGF)0$$aScholz, Henrike$$b3
000826187 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b4
000826187 773__ $$a10.1109/ICMLA.2016.0133
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