Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/934
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dc.contributor.authorKoyuncu, Hasan-
dc.contributor.authorCeylan, Rahime-
dc.date.accessioned2021-12-13T10:32:11Z-
dc.date.available2021-12-13T10:32:11Z-
dc.date.issued2018-
dc.identifier.isbn978-1-5386-6463-6-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/934-
dc.description13th IEEE International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) -- SEP 11-14, 2018 -- Lviv, UKRAINEen_US
dc.description.abstractOptimization based multithresholding techniques operates a cost function in order to segment an image via the obtained threshold values. For better segmentation results, a satisfier cost function and a robust optimization algorithm that is compatible with the used cost function, are needed. In this study, Scout particle swarm optimization (ScPSO) containing the efficient parts of Particle Swarm Optimization (PSO) and Artificial Bee Colony Optimization (ABC) is chosen for the optimization based process. As being the cost function, Kapur is preferred according to the advices in literature. Thus, KapurScPSO technique is formed for the task of image segmentation. For performance comparison, ScPSO is compared with PSO and Genetic Algorithm (GA) on segmentation of four well-known benchmarking images (Lena, Baboon, Hunter, Map). Standard deviations, objective values and Total Statistical Success (TSS) values are calculated for every algorithm at the evaluation of performances. All algorithms are employed 50 times to choose the best performance. Consequently, it's seen that Kapur-ScPSO achieves to better standard deviations and objective values than Kapur based PSO and GA algorithms on image segmentation. Furthermore, TSS values of proposed method are brilliant on both statistical metrics.en_US
dc.description.sponsorshipIEEE, IEEE Ukraine Sect, IEEE W Ukraine AP ED MTT CPMT SSC Soc Joint Chapter, Minist Educ & Sci Ukraine, Lviv Polytechn Natl Univ, Natl Acad Sci Ukraine, Inst Comp Sci & Informat Technologies, IEEE Ukraine Sect W MTT ED AP EP SSC Soc Joint Chapter, Tech Univ Lodz Poland, Inst Informat Technologiesen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2018 IEEE 13TH INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE ON COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES (CSIT), VOL 1en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectmultithresholdingen_US
dc.subjectimage segmentationen_US
dc.subjectparticle swarm optimizationen_US
dc.subjectKapur's entropy criterionen_US
dc.subjectscout particle swarm optimizationen_US
dc.subjectPSOen_US
dc.titleMultithresholding of Benchmark Images by A Novel Optimization Approachen_US
dc.typeConference Objecten_US
dc.identifier.scopus2-s2.0-85058007615en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.authoridKoyuncu, Hasan/0000-0003-4541-8833-
dc.authorwosidKoyuncu, Hasan/C-2203-2019-
dc.identifier.startpage322en_US
dc.identifier.endpage325en_US
dc.identifier.wosWOS:000456268700076en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextembargo_20300101-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.04. Department of Electrical and Electronics Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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