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dc.contributor.authorDhaubhadel, Prabal Man
dc.contributor.authorLee, Jong Kwan
dc.contributor.authorTian, Qing
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-25T19:43:17Z-
dc.date.available2024-07-25T19:43:17Z-
dc.date.issued2024
dc.identifier.citationWSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 67-76.en
dc.identifier.issn2464–4625 (online)
dc.identifier.issn2464–4617 (print)
dc.identifier.urihttp://hdl.handle.net/11025/57378
dc.description.sponsorshipThis research was supported in part by the David and Amy Fulton Endowed Professorship in Computer Sci ence at Bowling Green State University. This work would not have been possible without the computing resources provided by the Ohio Supercomputer Cen ter. We also thank the reviewers for their valuable com ments which improved our papecs_CZ
dc.description.sponsorshipThis research was supported in part by the David and Amy Fulton Endowed Professorship in Computer Sci ence at Bowling Green State University. This work would not have been possible without the computing resources provided by the Ohio Supercomputer Cen ter. We also thank the reviewers for their valuable com ments which improved our papeen
dc.format10 scs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectodšumovací autokodérycs
dc.subjectblok pozornostics
dc.subjectsegmentace solární koronální smyčkycs
dc.subjectvyplnění mezer smyčkycs
dc.titleAttention-Aware DAE for Automated Solar Coronal Loop Segmentationen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThis paper introduces an enhanced Denosing Autoencoder (DAE) model, incorporating a novel attention mecha nism, for the segmentation of solar coronal loops. This work is based on DAE framework to address the segmenta tion challenges posed by intricate structures of coronal loops which also appear with other solar features and image noises. Specifically, we introduce Encoding-Aware Decoding Attention (EADA) to all decoding stages of DAE, which resulted in improvement in coronal loop segmentation. Our models are validated through experiments on a synthetic image dataset of 11,000 images and a test dataset of 165 real coronal images of the NASA’s Solar Dy namics Observatory (SDO) satellite mission. Compared to the state-of-the-art coronal loop segmentation baseline, our attention-enhanced model results in better loop gap-filling and higher segmentation metrics (i.e., 3.6% increase in accuracy, 11.4% better recall and 5.6% higher precision).en
dc.subject.translateddenoising autoencodersen
dc.subject.translatedattention blocken
dc.subject.translatedSolar Coronal Loop Segmentationen
dc.subject.translatedloop gap-fillingen
dc.identifier.doihttps://doi.org/10.24132/10.24132/CSRN.3401.8
dc.type.statusPeer revieweden
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