A new 3D segmentation approach using extreme learning machine algorithm and morphological operations

Basit Öğe Kaydı
dc.authorid0000-0002-8611-701Xen_US
dc.contributor.authorKaya, Ertuğrul
dc.contributor.authorSert, Eser
dc.date.accessioned2021-11-25T09:02:04Z
dc.date.available2021-11-25T09:02:04Z
dc.date.issued2020en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractSegmentation is one of the most crucial steps of image processing. Because 3D images contain depth information, they have gradually gained importance for numerical systems in image analysis. In the present study, a new 3D segmentation method based on extreme learning machine and morphological operations (3DS-ELM) is proposed. The present study benefits from extreme learning machine (ELM) algorithm, which is a novel and fast learning algorithm for single-hidden layer feedforward networks (SLFNs), for training objects. Because a 3D model contains many points, direct segmentation on a 3D model is time-consuming and causes problems in the segmentation process, the proposed approach minimizes these problems and offers a quick and high-performance 3D segmentation method that can be used in various industrial fields. The proposed 3DS-ELM was compared with different approaches in order to analyze its 3D segmentation performance. Experimental studies proved that the proposed 3DS-ELM performed better than other approaches.en_US
dc.identifier.citationKaya, E., & Sert, E. (2020). A new 3D segmentation approach using extreme learning machine algorithm and morphological operations. Computers & Electrical Engineering, 84, 1-14, 106638.en_US
dc.identifier.doi10.1016/j.compeleceng.2020.106638
dc.identifier.endpage14en_US
dc.identifier.issn0045-7906en_US
dc.identifier.issn1879-0755en_US
dc.identifier.scopus2-s2.0-85083342209en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compeleceng.2020.106638
dc.identifier.urihttps://hdl.handle.net/20.500.12899/491
dc.identifier.volume84en_US
dc.identifier.wosWOS:000579053300013en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorSert, Eser
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofComputers & Electrical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectExtreme learning machineen_US
dc.subjectSegmentationen_US
dc.subject3D segmentationen_US
dc.subjectArtificial neural networken_US
dc.subjectFuzzy C-meansen_US
dc.titleA new 3D segmentation approach using extreme learning machine algorithm and morphological operationsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
Ertugrul Kaya - Makale Dosyası.pdf
Boyut:
2.06 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Full Text / Tam Metin
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Bilgisayar Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu