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DC poleHodnotaJazyk
dc.contributor.authorMhedhbi, Makrem
dc.contributor.authorMhiri, Slim
dc.contributor.authorGhorbel, Faouzi
dc.contributor.editorSkala, Václav
dc.date.accessioned2022-09-01T09:14:59Z-
dc.date.available2022-09-01T09:14:59Z-
dc.date.issued2022
dc.identifier.citationWSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 119-127.en
dc.identifier.isbn978-80-86943-33-6
dc.identifier.issn2464-4617
dc.identifier.urihttp://hdl.handle.net/11025/49585
dc.format9 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectpopis obrysucs
dc.subjectklasifikace tvarucs
dc.subjectCNNcs
dc.subjecthluboké učenícs
dc.subjectaugmentace datcs
dc.subjectvývoj křivkycs
dc.titleA new deep convolutional neural network for 2D contour classificationen
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedIn this paper, we present a new deep convolutional neural network to classify 2d contours, described by a sequence of points coordinates representing the boundary of objects. Several works dealt with this subject, even those using learning, but few works use deep learning. This is due to the fact that contours data are very narrow and inappropriate for convolution. To enrich this representation, we use curve evolution and consider simultaneously a multi-scale representation of a contour. Associated with coordinates, curvature estimated at each point is the most used descriptor who can help distinguishing objects. Despite deficiency of large 2d contour datasets, required for a convergent learning, the use of several additional techniques, such as data augmentation, lead to results outperforming the state of the art. We train ContourNet on MPEG-7 database CE-1 part B, witch achieves 100% for Top-1 accuracy rate on MPEG-7 test set, and 91.78% on Kimia216 dataset.en
dc.subject.translatedcontour descriptionen
dc.subject.translatedshape classificationen
dc.subject.translatedCNNen
dc.subject.translateddeep learningen
dc.subject.translateddata augmentationen
dc.subject.translatedcurve evolutionen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3201.15
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2022: Full Papers Proceedings

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