Bayesian deep learning for semantic segmentation of food images

dc.contributor.author	Aguilar, E.
dc.contributor.author	Nagarajan, B.
dc.contributor.author	Remeseiro López, Beatriz
dc.contributor.author	Radeva, P.
dc.date.accessioned	2023-03-02T09:30:08Z
dc.date.available	2023-03-02T09:30:08Z
dc.date.issued	2022
dc.identifier.citation	Computers and Electrical Engineering, 103 (2022); doi:10.1016/j.compeleceng.2022.108380
dc.identifier.issn	0045-7906
dc.identifier.uri	http://hdl.handle.net/10651/66714
dc.description.sponsorship	This 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.iso	eng
dc.relation.ispartof	Computers and electrical engineering
dc.rights	© 2022 Elsevier Ltd.
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138088096&doi=10.1016%2fj.compeleceng.2022.108380&partnerID=40&md5=a2031afffd3599299783dc126ac20699
dc.title	Bayesian deep learning for semantic segmentation of food images
dc.type	journal article
dc.identifier.doi	10.1016/j.compeleceng.2022.108380
dc.relation.projectID	TIN2018-095232-B-C21
dc.relation.projectID	PID2019-109238GB-C21
dc.relation.publisherversion	http://dx.doi.org/10.1016/j.compeleceng.2022.108380
dc.rights.accessRights	embargoed access
dc.type.hasVersion	AM