Contribution to a conference proceedings/Contribution to a book

FZJ-2017-00433

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Automatic Object Detection using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay

Bodenstein, C. (Corresponding author)FZJ* ; Götz, M.FZJ* ; Jansen, A. ; Scholz, H. ; Riedel, M.FZJ*

2017
IEEE

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Please use a persistent id in citations: http://hdl.handle.net/2128/13829  doi:10.1109/ICMLA.2016.0133

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.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 512 - Data-Intensive Science and Federated Computing (POF3-512) (POF3-512)

Appears in the scientific report 2017

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