On the relevance of the metadata used in the semantic segmentation of indoor image spaces
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].
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | Perú |
| Institución: | Consejo Nacional de Ciencia Tecnología e Innovación |
| Repositorio: | CONCYTEC-Institucional |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.concytec.gob.pe:20.500.12390/3030 |
| Acceso en línea: | https://hdl.handle.net/20.500.12390/3030 https://doi.org/10.1016/j.eswa.2021.115486 |
| Access Level: | acceso abierto |
| Palabra clave: | U-net Deep learning Fully convolutional network Indoor scenes Metadata preprocessing Semantic segmentation https://purl.org/pe-repo/ocde/ford#2.02.04 |
| Sumario: | 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]. |
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