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Bayesian deep learning for semantic segmentation of food images

dc.contributor.authorAguilar, E.
dc.contributor.authorNagarajan, B.
dc.contributor.authorRemeseiro López, Beatriz 
dc.contributor.authorRadeva, P.
dc.date.accessioned2023-03-02T09:30:08Z
dc.date.available2023-03-02T09:30:08Z
dc.date.issued2022
dc.identifier.citationComputers and Electrical Engineering, 103 (2022); doi:10.1016/j.compeleceng.2022.108380
dc.identifier.issn0045-7906
dc.identifier.urihttp://hdl.handle.net/10651/66714
dc.description.sponsorshipThis work was partially funded by 20211005001-VRIDT-UCN, TIN2018-095232-B-C21, PID2019-109238GB-C21, Measurer EIT Digital, Logmeal4Shape, and CERCA Programme/Generalitat de Catalunya, Spain. B. Nagarajan acknowledges the support of FPI Becas, MICINN, Spain.
dc.language.isoeng
dc.relation.ispartofComputers and electrical engineering
dc.rights© 2022 Elsevier Ltd.
dc.rightsCC Reconocimiento – No Comercial – Sin Obra Derivada 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceScopus
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85138088096&doi=10.1016%2fj.compeleceng.2022.108380&partnerID=40&md5=a2031afffd3599299783dc126ac20699
dc.titleBayesian deep learning for semantic segmentation of food images
dc.typejournal article
dc.identifier.doi10.1016/j.compeleceng.2022.108380
dc.relation.projectIDTIN2018-095232-B-C21
dc.relation.projectIDPID2019-109238GB-C21
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.compeleceng.2022.108380
dc.rights.accessRightsopen access
dc.type.hasVersionAM


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