Publication:
Surface roughness classification of mangosteen with gray level co-occurrence matrix based texture analysis

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.date.issuedBE2561
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.format.mimetypeapplication/pdf
dc.identifier.citation2018 22nd International Computer Science and Engineering Conference, ICSEC 2018.
dc.identifier.doi10.1109/ICSEC.2018.8712705
dc.identifier.other2-s2.0-85066475685
dc.identifier.urihttps://hdl.handle.net/20.500.14740/3942
dc.rights.holderมหาวิทยาลัยศรีนครินทรวิโรฒ
dc.subject.otherFruits
dc.subject.otherImage analysis
dc.subject.otherImage processing
dc.subject.otherImage texture
dc.subject.otherSurface measurement
dc.subject.otherTextures
dc.subject.otherExport potential
dc.subject.otherGLCM
dc.subject.otherGray level co occurrence matrix(GLCM)
dc.subject.otherGray level co-occurrence matrix
dc.subject.otherMangosteen
dc.subject.otherNondestructive methods
dc.subject.otherTextural feature
dc.subject.otherTexture analysis
dc.subject.otherSurface roughness
dc.titleSurface roughness classification of mangosteen with gray level co-occurrence matrix based texture analysis
dc.typeConference Paper
dspace.entity.typePublication
swu.datasource.scopushttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85066475685&doi=10.1109%2fICSEC.2018.8712705&partnerID=40&md5=644365631a5ba41bd5a96f7ab768b43d

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