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https://hdl.handle.net/20.500.13091/934
Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Koyuncu, Hasan | - |
dc.contributor.author | Ceylan, Rahime | - |
dc.date.accessioned | 2021-12-13T10:32:11Z | - |
dc.date.available | 2021-12-13T10:32:11Z | - |
dc.date.issued | 2018 | - |
dc.identifier.isbn | 978-1-5386-6463-6 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/934 | - |
dc.description | 13th IEEE International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) -- SEP 11-14, 2018 -- Lviv, UKRAINE | en_US |
dc.description.abstract | Optimization 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.sponsorship | IEEE, 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 Technologies | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2018 IEEE 13TH INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE ON COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES (CSIT), VOL 1 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | multithresholding | en_US |
dc.subject | image segmentation | en_US |
dc.subject | particle swarm optimization | en_US |
dc.subject | Kapur's entropy criterion | en_US |
dc.subject | scout particle swarm optimization | en_US |
dc.subject | PSO | en_US |
dc.title | Multithresholding of Benchmark Images by A Novel Optimization Approach | en_US |
dc.type | Conference Object | en_US |
dc.identifier.scopus | 2-s2.0-85058007615 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.authorid | Koyuncu, Hasan/0000-0003-4541-8833 | - |
dc.authorwosid | Koyuncu, Hasan/C-2203-2019 | - |
dc.identifier.startpage | 322 | en_US |
dc.identifier.endpage | 325 | en_US |
dc.identifier.wos | WOS:000456268700076 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
item.openairetype | Conference Object | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | embargo_20300101 | - |
item.languageiso639-1 | en | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 02.04. 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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Multithresholding_of_Benchmark_Images_by_A_Novel_Optimization_Approach.pdf Until 2030-01-01 | 405.57 kB | Adobe PDF | View/Open Request a copy |
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