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dc.contributor.authorOrtegon Aguilar, Jaime Silverio
dc.contributor.authorLedesma Alonso, Rene
dc.contributor.authorBarbosa Pool, Gliserio Romeli
dc.contributor.authorVazquez Castillo, Javier
dc.contributor.authorCastillo Atoche, Alejandro Arturo
dc.date.accessioned2021-02-11T03:14:34Z
dc.date.available2021-02-11T03:14:34Z
dc.date.issued2018
dc.identifier.issnhttps://doi.org/10.1016/j.commatsci.2018.02.054
dc.identifier.urihttp://hdl.handle.net/20.500.12249/2412
dc.description.abstractThe pixel's classi_cation of images obtained from random heterogeneous materials is a relevant step to compute their physical properties, like E_ective Transport Coecients (ETC), during a characterization process as stochastic reconstruction. A bad classi_cation will impact on the computed properties; however, the literature on the topic discusses mainly the correlation functions or the properties formulae, giving little or no attention to the classi_cation; authors mention either the use of a threshold or, in few cases, the use of Otsu's method. This paper presents a classi_cation approach based on Support Vector Machines (SVM) and a comparison with the Otsu's-based approach, based on accuracy and precision. The data used for the SVM training are the key for a better classi_cation; these data are the grayscale value, the magnitude and direction of pixels gradient. For instance, in the case study, the accuracy of the pixel's classi_cation is 77.6% for the SVM method and 40.9% for Otsu's method. Finally, a discussion about the impact on the correlation functions is presented in order to show the bene_ts of the proposal.
dc.description.provenanceSubmitted by Yeni Martin Cahum (yenimartin@uqroo.edu.mx) on 2021-02-11T03:14:34Z No. of bitstreams: 1 Material-phase-classification-by-mears-of-Support-Vector-Machines.pdf: 708313 bytes, checksum: a4a9fa7cb1f0b97545ca600e2375590c (MD5)
dc.description.provenanceMade available in DSpace on 2021-02-11T03:14:34Z (GMT). No. of bitstreams: 1 Material-phase-classification-by-mears-of-Support-Vector-Machines.pdf: 708313 bytes, checksum: a4a9fa7cb1f0b97545ca600e2375590c (MD5) Previous issue date: 2018
dc.formatpdf
dc.language.isoeng
dc.publisherElsevier
dc.relationComputational Materials Science
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.sourceComputational Materials Science
dc.subjectMáquinas de soporte vectorial
dc.subject.classificationINGENIERÍA Y TECNOLOGÍA::CIENCIAS TECNOLÓGICAS
dc.titleMaterial phase classification by mears of Support Vector Machines.
dc.typeArtículo
dc.type.conacytarticle
dc.rights.accesopenAccess
dc.identificator7||33
dc.audiencegeneralPublic
dc.identifier.doihttps://doi.org/10.1016/j.commatsci.2018.02.054
dc.date.revista2018
dc.number.revista148
dc.divisionBiblioteca Unidad Académica Chetumal, Santiago Pacheco Cruz


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