Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/12944
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dc.contributor.authorAcharya A.
dc.contributor.authorPhothisonothai M.
dc.contributor.authorTantisatirapong S.
dc.date.accessioned2021-04-05T03:21:52Z-
dc.date.available2021-04-05T03:21:52Z-
dc.date.issued2018
dc.identifier.other2-s2.0-85066475685
dc.identifier.urihttps://ir.swu.ac.th/jspui/handle/123456789/12944-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85066475685&doi=10.1109%2fICSEC.2018.8712705&partnerID=40&md5=644365631a5ba41bd5a96f7ab768b43d
dc.description.abstractMangosteen is one of the fruits that has an enormous export potential in Thailand. It is well-known as the queen of fruit. Mangosteen export generates large revenue; however, fruit is not defect free it contains many undesirable external as well as internal condition which results in the shipment rejection and decrease the reliability of the export. Therefore, this research investigates an approach for texture image analysis based surface roughness detection and classification into 3 classes: i.e., Glossy Surface, Mid Rough Surface and Extreme Rough Surface. In this study, for the first time, we propose the textural features extracted using Gray-Level Co-occurrence Matrix (GLCM) for surface roughness classification of mangosteen. © 2018 IEEE.
dc.subjectFruits
dc.subjectImage analysis
dc.subjectImage processing
dc.subjectImage texture
dc.subjectSurface measurement
dc.subjectTextures
dc.subjectExport potential
dc.subjectGLCM
dc.subjectGray level co occurrence matrix(GLCM)
dc.subjectGray level co-occurrence matrix
dc.subjectMangosteen
dc.subjectNondestructive methods
dc.subjectTextural feature
dc.subjectTexture analysis
dc.subjectSurface roughness
dc.titleSurface roughness classification of mangosteen with gray level co-occurrence matrix based texture analysis
dc.typeConference Paper
dc.rights.holderScopus
dc.identifier.bibliograpycitation2018 22nd International Computer Science and Engineering Conference, ICSEC 2018.
dc.identifier.doi10.1109/ICSEC.2018.8712705
Appears in Collections:Scopus 1983-2021

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