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dc.contributor.authorCarranza-Rojas, José
dc.contributor.authorGoeau, Herve 
dc.contributor.authorBonnet, Pierre 
dc.contributor.authorMata-Montero, Erick
dc.contributor.authorJoly, Alexis 
dc.date.accessioned2017-08-28T17:02:41Z
dc.date.available2017-08-28T17:02:41Z
dc.date.issued2017-08-11
dc.identifierhttps://doi.org/10.1186/s12862-017-1014-zes
dc.identifier.citationJose Carranza-Rojas, Herve Goeau, Pierre Bonnet V, Erick Mata-Montero, Alexis Joly. 2017. "Going deeper in the automated identification of Herbarium specimens" BMC Evolutionary Biology. volumen 17, número 181.es
dc.identifier.issn1471-2148
dc.identifier.urihttps://hdl.handle.net/2238/7326
dc.description.abstractHundreds of herbarium collections have accumulated a valuable heritage and knowledge of plants over several centuries. Recent initiatives started ambitious preservation plans to digitize this information and make it available to botanists and the general public through web portals. However, thousands of sheets are still unidentified at the species level while numerous sheets should be reviewed and updated following more recent taxonomic knowledge. These annotations and revisions require an unrealistic amount of work for botanists to carry out in a reasonable time. Computer vision and machine learning approaches applied to herbarium sheets are promising but are still not well studied compared to automated species identification from leaf scans or pictures of plants in the field.es
dc.language.isoenges
dc.publisherBioMed Centrales
dc.sourceRevista: BMC Evolutionary Biologyes
dc.subjectInformáticaes
dc.subjectBiodiversidades
dc.subjectVisión artificiales
dc.subjectBotánicaes
dc.subjectIdentificaciónes
dc.subjectAprendizajees
dc.subjectResearch Subject Categories::NATURAL SCIENCES::Biology::Organism biology::Plant physiologyes
dc.subjectResearch Subject Categories::TECHNOLOGY::Information technology::Image analysises
dc.titleGoing deeper in the automated identification of Herbarium specimenses
dc.typeartículo originales


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