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dc.contributor.authorCobo Cano, Miriam 
dc.contributor.authorHeredia Cacha, Ignacio 
dc.contributor.authorAguilar Gómez, Fernando 
dc.contributor.authorLloret Iglesias, Lara
dc.contributor.authorGarcía Díaz, Daniel 
dc.contributor.authorBartolomé, Begoña
dc.contributor.authorMoreno-Arribas, Victoria M.
dc.contributor.authorYuste, Silvia
dc.contributor.authorPérez-Matute, Patricia
dc.contributor.authorMotilva, Maria-Jose
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2024-02-21T11:53:15Z
dc.date.available2024-02-21T11:53:15Z
dc.date.issued2022
dc.identifier.issn2405-8440
dc.identifier.urihttps://hdl.handle.net/10902/31857
dc.description.abstractIn this paper, we present a method to determine the volume of wine in different types of glass liquid containers from a single-view image. The proposed model predicts red wine volume from a photograph of the glass containing the wine. Experimental results demonstrated satisfactory performance of our image-based wine measurement system, with a Mean Absolute Error lower than 10 mL. To train and evaluate our system, we introduced the WineGut_BrainUp dataset, a new dataset of glasses of wine that contains 24305 laboratory images, including a wide range of containers, volumes of wine, backgrounds, object distances, angles and lightning, with or without calibration object. The proposed methodology is a suitable analytical tool for automate measurement of red wine volume. Indeed, it has potential real life applications in diet monitoring and wine consumption studies.es_ES
dc.format.extent7 p.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationales_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceHeliyon, 2022, 8, e10557es_ES
dc.subject.otherDeep learning modeles_ES
dc.subject.otherQuantitative red wine volume estimationes_ES
dc.subject.otherSingle-view imagees_ES
dc.titleArtificial intelligence to estimate wine volume from single-view imageses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1016/j.heliyon.2022.e10557es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1016/j.heliyon.2022.e10557
dc.type.versionpublishedVersiones_ES


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Attribution-NonCommercial-NoDerivatives 4.0 InternationalExcepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 International