Publicación:
On the relevance of the metadata used in the semantic segmentation of indoor image spaces
On the relevance of the metadata used in the semantic segmentation of indoor image spaces
| dc.contributor.author | Vasquez-Espinoza L. | es_PE |
| dc.contributor.author | Castillo-Cara M. | es_PE |
| dc.contributor.author | Orozco-Barbosa L. | es_PE |
| dc.date.accessioned | 2024-05-30T23:13:38Z | |
| dc.date.available | 2024-05-30T23:13:38Z | |
| dc.date.issued | 2021 | |
| dc.description | This work has been partially funded by the Spanish Ministry of Sci-ence, Education and Universities, the European Regional DevelopmentFund and the State Research Agency [grant number RTI2018-098156-B-C52], and by FONDECYT / World Bank [grant number 026-2019FONDECYT-BM-INC.INV]. | |
| dc.description.abstract | The 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. © 2021 The Author(s) | |
| dc.description.sponsorship | Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec | |
| dc.identifier.doi | https://doi.org/10.1016/j.eswa.2021.115486 | |
| dc.identifier.scopus | 2-s2.0-85109921392 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12390/3030 | |
| dc.language.iso | eng | |
| dc.publisher | Elsevier Ltd | |
| dc.relation.ispartof | Expert Systems with Applications | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | U-net | |
| dc.subject | Deep learning | es_PE |
| dc.subject | Fully convolutional network | es_PE |
| dc.subject | Indoor scenes | es_PE |
| dc.subject | Metadata preprocessing | es_PE |
| dc.subject | Semantic segmentation | es_PE |
| dc.subject.ocde | https://purl.org/pe-repo/ocde/ford#2.02.04 | |
| dc.title | On the relevance of the metadata used in the semantic segmentation of indoor image spaces | |
| dc.type | info:eu-repo/semantics/article | |
| dspace.entity.type | Publication |