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dc.contributor.authorKucukugurlu, Busranur
dc.contributor.authorGedikli, Eyup
dc.date.accessioned2021-11-09T19:43:16Z
dc.date.available2021-11-09T19:43:16Z
dc.date.issued2019
dc.identifier.isbn978-1-7281-1904-5
dc.identifier.issn2165-0608
dc.identifier.urihttps://hdl.handle.net/20.500.12440/3588
dc.description27th Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2019 -- Sivas Cumhuriyet Univ, Sivas, TURKEYen_US
dc.description.abstractThresholding is a very important stage in computer vision applications. An ideal single thresholding algorithm is not available for all environments. Multi-level thresholding in environments with multiple objects is constantly being developed for interpretation of images. Kapur entropy and Otsu approaches are among the most successful algorithms in the literature. In this study, it is tried to increase the performance of Otsu and Kapur algorithms by using meta-heuristic optimization approaches. The results of the Firefly Algorithm (FF) and Real Coded Genetic Algorithm (RGA) were evaluated with PSNR, SSIM and CPU processing time criteria.en_US
dc.description.sponsorshipIEEE Turkey Sect, Turkcell, Turkhavacilik Uzaysanayii, Turitak Bilgem, Gebze Teknik Univ, SAP, Detaysoft, NETAS, Havelsanen_US
dc.language.isoturen_US
dc.publisherIEEEen_US
dc.relation.ispartof2019 27th Signal Processing and Communications Applications Conference (Siu)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFirefly Algorithmen_US
dc.subjectReal Coded Genetic Algorithmen_US
dc.subjectOtsu Methoden_US
dc.subjectKapur's Entropyen_US
dc.subjectThresholdingen_US
dc.titleMeta-Heuristic Algorithms Based Multi-Level Thresholdingen_US
dc.typeconferenceObjecten_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.description.wospublicationidWOS:000518994300018en_US
dc.description.scopuspublicationid2-s2.0-85071978121en_US
dc.departmentGümüşhane Üniversitesien_US
dc.authorscopusid57210944199
dc.authorscopusid8507392800


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