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dc.contributor.authorVasquez Espinoza L.
dc.contributor.authorCastillo Cara, José Manuel
dc.contributor.authorOrozco Barbosa L.
dc.contributor.otherCastillo Cara, José Manuel
dc.date.accessioned2021-08-02T17:00:40Z
dc.date.available2021-08-02T17:00:40Z
dc.date.issued2021
dc.identifier.citationVasquez-Espinoza, L., Castillo-Cara, J. M. & Orozco-Barbosa L. (2021). On the relevance of the metadata used in the semantic segmentation of indoor image spaces. Expert Systems with Applications, 184. https://doi.org/10.1016/j.eswa.2021.115486es_PE
dc.identifier.issn0957-4174
dc.identifier.urihttps://hdl.handle.net/20.500.12724/13669
dc.description.abstractThe study of artificial learning processes in the area of computer vision context has mainly focused on achieving a fixed output target rather than on identifying the underlying processes as a means to develop solutions capable of performing as good as or better than the human brain. This work reviews the well-known segmentation efforts in computer vision. However, our primary focus is on the quantitative evaluation of the amount of contextual information provided to the neural network. In particular, the information used to mimic the tacit information that a human is capable of using, like a sense of unambiguous order and the capability of improving its estimation by complementing already learned information. Our results show that, after a set of pre and post-processing methods applied to both the training data and the neural network architecture, the predictions made were drastically closer to the expected output in comparison to the cases where no contextual additions were provided. Our results provide evidence that learning systems strongly rely on contextual information for the identification task process.en_EN
dc.formatapplication/html
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofurn:issn:0957-4174
dc.rightsinfo:eu-repo/semantics/openAccess*
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.sourceRepositorio Institucional Ulima
dc.sourceUniversidad de Lima
dc.subjectComputer visionen_EN
dc.subjectDeep learning (Machine learning)en_EN
dc.subjectVisión por computadoraes_PE
dc.subjectAprendizaje profundo (Aprendizaje automático)es_PE
dc.subject.classificationPendientees_PE
dc.titleOn the relevance of the metadata used in the semantic segmentation of indoor image spacesen_EN
dc.typeinfo:eu-repo/semantics/article
dc.type.otherArtículo en Scopus
ulima.areas.lineasdeinvestigacionProductividad y empleo / Innovación: tecnologías y productoses_PE
dc.identifier.journalExpert Systems with Applications
dc.publisher.countryNL
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.04
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2021.115486
ulima.catOI
ulima.autor.afiliacionCastillo-Cara, José Manuel (Universidad de Lima)
ulima.autor.carreraIngeniería de Sistemas
dc.identifier.isni0000000121541816
dc.identifier.scopusid2-s2.0-85109921392


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