Exploration of U-Net in Automated Solar Coronal Loop Segmentation

dc.contributor.authorMoradi, Shadi
dc.contributor.authorLee, Jong Kwan
dc.contributor.authorTian, Qing
dc.contributor.editorSkala, Václav
dc.date.accessioned2021-09-01T06:11:06Z
dc.date.available2021-09-01T06:11:06Z
dc.date.issued2021
dc.description.abstract-translatedThis paper presents a deep convolutional neural network (CNN) based method that automatically segments arc-like structures of coronal loops from the intensity images of Sun’s corona. The method explores multiple U-Netarchitecture variants which enable segmentation of coronal loop structures of active regions from NASA’s SolarDynamic Observatory (SDO) imagery. The effectiveness of the method is evaluated through experiments on bothsynthetic and real images, and the results show that the method segments the coronal loop structures accurately.en
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationWSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 227-236.en
dc.identifier.doihttps://doi.org/10.24132/CSRN.2021.3101.25
dc.identifier.isbn978-80-86943-34-3
dc.identifier.issn2464-4617
dc.identifier.issn2464–4625(CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/45028
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.rights.accessopenAccessen
dc.subjectkonvoluční neuronová síťcs
dc.subjectU-Netcs
dc.subjectsegmentacecs
dc.subjectaplikace solární fyzikycs
dc.subject.translatedconvolutional neural networken
dc.subject.translatedU-Neten
dc.subject.translatedsegmentationen
dc.subject.translatedSolar Physics Applicationen
dc.titleExploration of U-Net in Automated Solar Coronal Loop Segmentationen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

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