Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/3254
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dc.contributor.authorJama, Bashir Sheikh Abdullahi-
dc.contributor.authorAkhan Baykan, Nurdan-
dc.date.accessioned2023-01-08T19:04:20Z-
dc.date.available2023-01-08T19:04:20Z-
dc.date.issued2020-
dc.identifier.issn2148-2683-
dc.identifier.urihttps://doi.org/10.31590/ejosat.812052-
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1136056-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/3254-
dc.description.abstractImage segmentation is a significant step in image processing that applies to various fields. These fields include machine vision, object detection, astronomy, biometric recognition systems (face, fingerprint, plate, and eye), medical imaging, video surveillance, and many other image-based technologies. Efficient image segmentation is one of the most important tasks and critical roles in automatic image processing. Especially in engineering studies, finding the most suitable solutions for problems is one of the important research topics. Bio-inspired algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Bat Algorithm (BAT), etc. are used to find the optimal solutions in search spaces and Ant Lion Optimization (ALO) is one of these algorithms. In recent years, bio-inspired algorithms are used to optimize the segmentation parameters of the images. This research proposes a modified region growing (RG) image segmentation approach using bio-inspired ALO. Region growing (RG) has three main problems as the selection of the right seeds, the number of seeds, and the region growing strategy. Therefore, ALO was used to solve seed selection problems in RG. In this study, firstly, the median filter was applied to the inputs to improve the quality of the images. Subsequently, the region growing segmentation was carried out using optimal seed points obtained from the ALO. For obtaining the optimal seeds, ALO was used to solve the limitations of RG during the segmentation process. The success of the proposed approach was tested using some images taken from the BSDS300 (Berkeley) dataset. The experimental results show that the proposed method segments almost all the images.en_US
dc.language.isoenen_US
dc.relation.ispartofAvrupa Bilim ve Teknoloji Dergisien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRegion growingen_US
dc.subjectSeed point selectionen_US
dc.subjectImage Segmentationen_US
dc.subjectpre-processingen_US
dc.subjectAnt Lion Optimization Bölge büyütmeen_US
dc.subjecttohum seçimien_US
dc.subjectgörüntü bölütlemeen_US
dc.subjectönişlemeen_US
dc.subjectKarınca Aslan Optimizasyonuen_US
dc.titleModified Region Growing Method For Image Segmentation Using Ant Lion Optimization Algorithmen_US
dc.typeArticleen_US
dc.identifier.doi10.31590/ejosat.812052-
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.identifier.volume0en_US
dc.identifier.issueEjosat Özel Sayı 2020 (ICCEES)en_US
dc.identifier.startpage404en_US
dc.identifier.endpage411en_US
dc.institutionauthorAkhan Baykan, Nurdan-
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.trdizinid1136056en_US
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections
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