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dc.contributor | Facultad de Ciencias Biologicas y Ambientales | es_ES |
dc.contributor.author | Bueno García, María Gloria | |
dc.contributor.author | Déniz Suárez, Óscar | |
dc.contributor.author | Pedraza, Aníbal | |
dc.contributor.author | Ruiz-Santaquiteria Alegre, Jesús | |
dc.contributor.author | Salido Tercero, Jesús | |
dc.contributor.author | Cristóbal Pérez, Gabriel | |
dc.contributor.author | Borrego Ramos, María | |
dc.contributor.author | Blanco Lanza, Saúl | |
dc.contributor.other | Ecologia | es_ES |
dc.date | 2017 | |
dc.date.accessioned | 2024-03-22T07:58:45Z | |
dc.date.available | 2024-03-22T07:58:45Z | |
dc.identifier.citation | Bueno, G., Deniz, O., Pedraza, A., Ruiz-Santaquiteria, J., Salido, J., Cristóbal, G., Borrego-Ramos, M., & Blanco, S. (2017). Automated diatom classification (Part A): handcrafted feature approaches. Applied Sciences, 7(8) Article e753. https://doi.org/10.3390/APP7080753 | es_ES |
dc.identifier.other | https://www.mdpi.com/2076-3417/7/8/753 | es_ES |
dc.identifier.uri | https://hdl.handle.net/10612/19275 | |
dc.description | This article belongs to the Special Issue Automated Analysis and Identification of Phytoplankton Images | es_ES |
dc.description.abstract | [EN] This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for water quality assessment. The most relevant features for diatom description and classification have been selected using an extensive dataset of 80 taxa with a minimum of 100 samples/taxon augmented to 300 samples/taxon. In addition to published morphological, statistical and textural descriptors, a new textural descriptor, Local Binary Patterns (LBP), to characterize the diatom’s valves, and a log Gabor implementation not tested before for this purpose are introduced in this paper. Results show an overall accuracy of 98.11% using bagging decision trees and combinations of descriptors. Finally, some phycological features of diatoms that are still difficult to integrate in computer systems are discussed for future work | es_ES |
dc.language | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Biología | es_ES |
dc.subject | Ecología. Medio ambiente | es_ES |
dc.subject.other | Feature analysis | es_ES |
dc.subject.other | Textural features | es_ES |
dc.subject.other | Automatic classification | es_ES |
dc.subject.other | Handcrafted approaches | es_ES |
dc.subject.other | Diatoms | es_ES |
dc.title | Automated Diatom Classification (Part A): Handcrafted Feature Approaches | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.identifier.doi | 10.3390/app7080753 | |
dc.description.peerreviewed | SI | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO/Programa Estatal de I+D+I Orientada a los Retos de la Sociedad/CTM2014-51907-C2-R/ES/Desarrollo de métodos automáticos de identificación de diatomeas en el análisis cuantitativo y monitorización de la calidad de agua/AQUALITAS | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.essn | 2076-3417 | |
dc.journal.title | Applied Sciences | es_ES |
dc.volume.number | 7 | es_ES |
dc.issue.number | 8 | es_ES |
dc.page.initial | 753 | es_ES |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es_ES |
dc.subject.unesco | 2417.07 Algología (Ficología) | es_ES |
dc.subject.unesco | 2417.20 Taxonomía Vegetal | es_ES |
dc.description.project | The authors acknowledge financial support of the Spanish Government under the Aqualitas-retos project (Ref. CTM2014-51907-C2-2-R-MINECO), http://aqualitas-retos.es/en/ | es_ES |
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