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dc.contributor.authorPAILLASSA, M.
hal.structure.identifierDepartment of Theoretical Physics [DTP]
dc.contributor.authorBERTIN, E.
hal.structure.identifierM2A 2018
dc.contributor.authorBOUY, H.
dc.date.issued2018
dc.date.conference2018-07-03
dc.description.abstractEnIn this work, we propose to use convolutional neural networks to detect contaminants in astronomical images. Each contaminant is treated in a one vs all fashion. Once trained, our network is able to detect various contaminants such as cosmic rays, hot and bad pixel defaults, persistence effects, satellite trails or fringe patterns in images of various field properties. The convolutional neural network is performing semantic segmentation: it can output a probability map, assigning to each pixel its probability to belong to the contaminant or the background class. Training and testing data have been gathered from real or simulated data.
dc.language.isoen
dc.subject.enconvolutional neural networks
dc.subject.enastronomical image analysis
dc.subject.enastronomical image contaminants
dc.title.enIdentifying contaminants in astronomical images using convolutional neural networks
dc.typeCommunication dans un congrès
dc.subject.halPlanète et Univers [physics]/Astrophysique [astro-ph]/Instrumentation et méthodes pour l'astrophysique [astro-ph.IM]
bordeaux.countryFR
bordeaux.conference.citybordeaux
bordeaux.peerReviewedoui
hal.identifierhal-01982125
hal.version1
hal.invitednon
hal.proceedingsnon
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01982125v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2018&rft.au=PAILLASSA,%20M.&BERTIN,%20E.&BOUY,%20H.&rft.genre=unknown


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