2026-05-12T08:08:54Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/73782023-03-22T12:05:54Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Diez Pastor, José Francisco 156 600 0000-0001-5013-7505 Latorre Carmona, Pedro 815 600 0000-0001-6984-5173 Arnaiz González, Álvar 39 600 0000-0001-6965-0237 Ruiz Pérez, Javier 76ebab67-22e8-4c26-b0c8-92e7dca9df10 600 Zurro, Débora 2c9d15de-83d0-46e0-9c8b-c9ac1d38841c 600 2023-02-06T07:40:24Z 2023-02-06T07:40:24Z 2020-11 1431-9276 http://hdl.handle.net/10259/7378 10.1017/S1431927620024629 1435-8115 Phytoliths can be an important source of information related to environmental and climatic change, as well as to ancient plant use by humans, particularly within the disciplines of paleoecology and archaeology. Currently, phytolith identification and categorization is performed manually by researchers, a time-consuming task liable to misclassifications. The automated classification of phytoliths would allow the standardization of identification processes, avoiding possible biases related to the classification capability of researchers. This paper presents a comparative analysis of six classification methods, using digitized microscopic images to examine the efficacy of different quantitative approaches for characterizing phytoliths. A comprehensive experiment performed on images of 429 phytoliths demonstrated that the automatic phytolith classification is a promising area of research that will help researchers to invest time more efficiently and improve their recognition accuracy rate. This work was supported by the project TIN2015- 67534-P (MINECO/FEDER, UE) of the Ministerio de Economía y Competitividad of the Spanish Government, by the project BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León (both projects co-financed through European Union FEDER funds) and by Grups de Recerca de Qualitat CaSEs – Culture and Socio-Ecological Dynamics (2017 SGR 212), AGAUR-Generalitat de Catalunya. The authors gratefully acknowledge the support of NVIDIA Corporation and its donation of the TITAN Xp GPUs used in this research. application/pdf eng Cambridge University Press Microscopy and Microanalysis. 2020, V. 26, n. 6, p. 1158-1167 https://doi.org/10.1017/S1431927620024629 info:eu-repo/grantAgreement/MINECO//TIN2015-67534-P/ES/ALGORITMOS DE ENSEMBLES PARA PROBLEMAS DE SALIDAS MULTIPLES. NUEVOS DESARROLLOS Y APLICACIONES/ info:eu-repo/grantAgreement/Junta de Castilla y León//BU085P17//Minería de datos para le mejora del mantenimiento y disponibilidad de máquinas de altas presiones/ info:eu-repo/grantAgreement/AGAUR//2017 SGR 212/ Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Feature extraction Machine learning Microfossils Morphometry Proxy Informática Computer science “You Are Not My Type”: An Evaluation of Classification Methods for Automatic Phytolith Identification info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Microscopy and Microanalysis 26 6 1158 1167 THUMBNAIL Díez-mm_2020.pdf.jpg Díez-mm_2020.pdf.jpg IM Thumbnail image/jpeg 4981 https://riubu.ubu.es/bitstream/10259/7378/4/D%c3%adez-mm_2020.pdf.jpg 129dfc924c893892cc405ab1862d43aa MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/7378/3/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/7378/2/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 2 ORIGINAL Díez-mm_2020.pdf Díez-mm_2020.pdf application/pdf 492567 https://riubu.ubu.es/bitstream/10259/7378/1/D%c3%adez-mm_2020.pdf e28d14cda9400f53e88d4981df585929 MD5 1 10259/7378 oai:riubu.ubu.es:10259/7378 2023-03-22 13:05:54.447 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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