Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/1214
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dc.contributor.authorSağlam, Ali-
dc.contributor.authorBaykan, Nurdan Akhan-
dc.date.accessioned2021-12-13T10:38:35Z-
dc.date.available2021-12-13T10:38:35Z-
dc.date.issued2021-
dc.identifier.issn2548-0960-
dc.identifier.issn2548-0960-
dc.identifier.urihttps://doi.org/10.26833/ijeg.709212-
dc.identifier.urihttps://app.trdizin.gov.tr/makale/TkRBNE56TXdNQT09-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/1214-
dc.description.abstractTwo important features of the points in the LiDAR point clouds are the spatial and the color features. The spatial feature is mostly used in the point cloud processing field due to its 3D informative and distinctive characteristic. The local geometric difference derived from the spatial features of the points is usually benefited by graph-based point cloud segmentation methods, because the geometric features of the local point groups are highly distinctive. In this paper, we use both the geometric and color differences of the adjacent local point groups at the impact rates 0.3, 0.5, and 0.7 and cooperate the Euclidean and the vector color differences within several averaging techniques for the color difference. The difference forms have been tested within a graph-based segmentation method on four point cloud segmentation datasets, two indoor and two outdoor, using their spatial and color information. The geometric mean as an averaging techniques increases the segmentation success for the all datasets except one outdoor when the color differences are used in the segmentation at the impact rate 0.3, while the harmonic mean increases the success for the all datasets the successes except the other outdoor at the same impact rate. According to the test results, the cooperating of the Euclidean and vector angular color difference measurements can considerable increase the segmentation success on the point clouds with color information in a high quality.en_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Engineering and Geosciencesen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleA new color distance measure formulated from the cooperation of the Euclidean and the vector angular differences for lidar point cloud segmentationen_US
dc.typeArticleen_US
dc.identifier.doi10.26833/ijeg.709212-
dc.identifier.scopus2-s2.0-85118971138en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.identifier.volume6en_US
dc.identifier.issue3en_US
dc.identifier.startpage117en_US
dc.identifier.endpage124en_US
dc.identifier.wosWOS:000608477000001en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.trdizinid408730en_US
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.03. Department of Computer Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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