Show simple item record

dc.contributor.authorMerino Bermejo, Ibon
dc.contributor.authorAzpiazu Lozano, Jon
dc.contributor.authorRemazeilles, Anthony
dc.contributor.authorSierra Araujo, Basilio ORCID
dc.date.accessioned2023-06-30T16:57:02Z
dc.date.available2023-06-30T16:57:02Z
dc.date.issued2023-07
dc.identifier.citationNeurocomputing 541 : (2023) // Article ID 126270es_ES
dc.identifier.issn0925-2312
dc.identifier.issn1872-8286
dc.identifier.urihttp://hdl.handle.net/10810/61846
dc.description.abstractDeep learning methods have revolutionized computer vision since the appearance of AlexNet in 2012. Nevertheless, 6 degrees of freedom pose estimation is still a difficult task to perform precisely. Therefore, we propose 2 ensemble techniques to refine poses from different deep learning 6DoF pose estimation models. The first technique, merge ensemble, combines the outputs of the base models geometrically. In the second, stacked generalization, a machine learning model is trained using the outputs of the base models and outputs the refined pose. The merge method improves the performance of the base models on LMO and YCB-V datasets and performs better on the pose estimation task than the stacking strategy.es_ES
dc.description.sponsorshipThis paper has been supported by the project PROFLOW under the Basque program ELKARTEK, grant agreement No. KK-2022/00024.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectdeep learninges_ES
dc.subjectpose estimationes_ES
dc.subjectensemblees_ES
dc.subjectstacked generalizationes_ES
dc.titleEnsemble of 6 DoF Pose estimation from state-of-the-art deep methods.es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)es_ES
dc.rights.holderAtribución-NoComercial-SinDerivadas 3.0 España*
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0925231223003934es_ES
dc.identifier.doi10.1016/j.neucom.2023.126270
dc.departamentoesCiencia de la computación e inteligencia artificiales_ES
dc.departamentoeuKonputazio zientziak eta adimen artifizialaes_ES


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Except where otherwise noted, this item's license is described as © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)