Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/43951
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dc.contributor.authorYadav, Anjalien_US
dc.contributor.authorDutta, Malay Kishoreen_US
dc.contributor.authorTravieso, Carlos M.en_US
dc.contributor.authorAlonso, Jesus B.en_US
dc.date.accessioned2018-11-21T19:06:36Z-
dc.date.available2018-11-21T19:06:36Z-
dc.date.issued2017en_US
dc.identifier.isbn9781538608500en_US
dc.identifier.urihttp://hdl.handle.net/10553/43951-
dc.description.abstractLeaf can be one of the many different parameters on the basis of which a plant can be uniquely identified. Many plants types are on the verge of extinction and can be taken care of, if identified correctly. The proposed method discusses an automated image processing system for leaf classification. The leaf pixels from the image are segmented and termed as region of interest (ROI). A set of geometrical, textural and statistical features is extracted for each input sample and analyzed using a multi class SVM classifier. The proposed system has achieved an accuracy of 97% with a sensitivity of 98.32%. The results are encouraging for a dataset consisting of 10 different leaf classes and can be used for development of some real time application.en_US
dc.languagespaen_US
dc.relation.ispartof2017 International Work Conference on Bio-Inspired Intelligence: Intelligent Systems for Biodiversity Conservation, IWOBI 2017 - Proceedingsen_US
dc.source2017 International Work Conference on Bio-Inspired Intelligence: Intelligent Systems for Biodiversity Conservation, IWOBI 2017 - Proceedings (7985531)en_US
dc.subject3307 Tecnología electrónicaen_US
dc.subject.otherSupport vector machines , Feature extraction , Image segmentation , Classification algorithms , Shape , Gray-scale, Image Processing , Features extraction , GLCM features , Classification , Multi-SVMen_US
dc.titleAutomatic Identification of Botanical Samples of leaves using Computer Visionen_US
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dc.typeConferenceObjectes
dc.relation.conference5th IEEE International Work Conference on Bio-Inspired Intelligence, IWOBI 2017
dc.identifier.doi10.1109/IWOBI.2017.7985531
dc.identifier.scopus85028556675
dc.contributor.authorscopusid57195513394
dc.contributor.authorscopusid35291803600
dc.contributor.authorscopusid6602376272
dc.contributor.authorscopusid24774957200
dc.identifier.issue7985531-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Actas de congresosen_US
dc.date.coverdateJulio 2017
dc.identifier.conferenceidevents121608
dc.identifier.ulpgces
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-4621-2768-
crisitem.author.orcid0000-0002-7866-585X-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameTravieso González, Carlos Manuel-
crisitem.author.fullNameAlonso Hernández, Jesús Bernardino-
crisitem.event.eventsstartdate10-07-2017-
crisitem.event.eventsenddate12-07-2017-
Appears in Collections:Actas de congresos
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