Multilevel image thresholding selection based on grey wolf optimizer

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dc.authorid0000-0003-1311-5918
dc.authorid0000-0001-5890-510X
dc.authorid0000-0002-2503-1482
dc.contributor.authorKoc, Ismail
dc.contributor.authorBaykan, Omer Kaan
dc.contributor.authorBabaoglu, Ismail
dc.date.accessioned2020-03-26T19:54:43Z
dc.date.available2020-03-26T19:54:43Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractMultilevel thresholding is an important image process technique for image processing and pattern recognition. Selecting an optimal threshold value is one of the most crucial phase in image thresholding. While bi-level segmentation contains separating the original image into subdivided sections with help of a threshold value, multilevel segmentation involves multi threshold values. Especially in multilevel image tresholding, the computational time of detailed search increases exponentially with the number of preferred thresholds. For compelling problems, swarm intelligence is known as one of the successful and influential optimization methods. In this paper, the grey wolf optimizer (GWO), a recently proposed swarm-based meta-heuristic which imitates the social leadership and hunting behavior of gray wolves in nature is employed for solving the multilevel image thresholding problem. The experimental results on standard benchmark images indicate that the grey wolf optimizer algorithm is comparable with other state of the art algorithms.en_US
dc.identifier.citationKoç, İ., Baykan, Ö. K., Babaoğlu, İ. (2018). Multilevel Image Thresholding Selection Based on Grey Wolf Optimizer. Journal of Polytechnic-Politeknik Dergisi, 21(4), 841-847.
dc.identifier.doi10.2339/politeknik.389613en_US
dc.identifier.endpage847en_US
dc.identifier.issn1302-0900en_US
dc.identifier.issn2147-9429en_US
dc.identifier.issue4en_US
dc.identifier.pmid#YOKen_US
dc.identifier.startpage841en_US
dc.identifier.urihttps://dx.doi.org/10.2339/politeknik.389613
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36785
dc.identifier.volume21en_US
dc.identifier.wosWOS:000448383600010en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorKoc, Ismail
dc.institutionauthorBaykan, Omer Kaan
dc.institutionauthorBabaoglu, Ismail
dc.language.isotren_US
dc.publisherGAZI UNIVen_US
dc.relation.ispartofJOURNAL OF POLYTECHNIC-POLITEKNIK DERGISIen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectMultilevel image thresholdingen_US
dc.subjectotsu methoden_US
dc.subjectherd intelligenceen_US
dc.subjectoptimization algorithmsen_US
dc.subjectgray wolf algorithmen_US
dc.titleMultilevel image thresholding selection based on grey wolf optimizeren_US
dc.typeArticleen_US

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