guest ::
| Home > Publications database > Automatic Object Detection using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay > print |
| Home > Publications database > Automatic Object Detection using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay > print |
| 001 | 826187 | ||
| 005 | 20210129225536.0 | ||
| 024 | 7 | _ | |a 10.1109/ICMLA.2016.0133 |2 doi |
| 024 | 7 | _ | |a 2128/13829 |2 Handle |
| 037 | _ | _ | |a FZJ-2017-00433 |
| 100 | 1 | _ | |a Bodenstein, Christian |0 P:(DE-Juel1)164357 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a 15th IEEE International Conference on Machine Learning and Applications |g IEEE ICMLA'16 |c Anaheim |d 2016-12-18 - 2016-12-20 |w USA |
| 245 | _ | _ | |a Automatic Object Detection using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay |
| 260 | _ | _ | |c 2017 |b IEEE |
| 295 | 1 | 0 | |a ISBN 978-1-5090-6167-9 |
| 300 | _ | _ | |a 746 - 751 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1486654205_14798 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Contribution to a book |0 PUB:(DE-HGF)7 |2 PUB:(DE-HGF) |m contb |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 512 - Data-Intensive Science and Federated Computing (POF3-512) |0 G:(DE-HGF)POF3-512 |c POF3-512 |f POF III |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef Conference |
| 700 | 1 | _ | |a Götz, Markus |0 P:(DE-Juel1)162390 |b 1 |
| 700 | 1 | _ | |a Jansen, Annika |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Scholz, Henrike |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Riedel, Morris |0 P:(DE-Juel1)132239 |b 4 |
| 773 | _ | _ | |a 10.1109/ICMLA.2016.0133 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/826187/files/Poster.pdf |y Restricted |
| 856 | 4 | _ | |x icon |u https://juser.fz-juelich.de/record/826187/files/Poster.gif?subformat=icon |y Restricted |
| 856 | 4 | _ | |x icon-1440 |u https://juser.fz-juelich.de/record/826187/files/Poster.jpg?subformat=icon-1440 |y Restricted |
| 856 | 4 | _ | |x icon-180 |u https://juser.fz-juelich.de/record/826187/files/Poster.jpg?subformat=icon-180 |y Restricted |
| 856 | 4 | _ | |x icon-640 |u https://juser.fz-juelich.de/record/826187/files/Poster.jpg?subformat=icon-640 |y Restricted |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/826187/files/paper.pdf |
| 856 | 4 | _ | |y OpenAccess |x icon |u https://juser.fz-juelich.de/record/826187/files/paper.gif?subformat=icon |
| 856 | 4 | _ | |y OpenAccess |x icon-1440 |u https://juser.fz-juelich.de/record/826187/files/paper.jpg?subformat=icon-1440 |
| 856 | 4 | _ | |y OpenAccess |x icon-180 |u https://juser.fz-juelich.de/record/826187/files/paper.jpg?subformat=icon-180 |
| 856 | 4 | _ | |y OpenAccess |x icon-640 |u https://juser.fz-juelich.de/record/826187/files/paper.jpg?subformat=icon-640 |
| 909 | C | O | |o oai:juser.fz-juelich.de:826187 |p openaire |p open_access |p driver |p VDB |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)164357 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)162390 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)132239 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-512 |2 G:(DE-HGF)POF3-500 |v Data-Intensive Science and Federated Computing |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |l Supercomputing & Big Data |
| 914 | 1 | _ | |y 2017 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a contrib |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a contb |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|