Hybrid method using EDMS gabor for shape and texture

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dc.contributor.authorSahib, Zubaidah Ali
dc.contributor.authorUçan, Osman Nuri
dc.contributor.authorTalab, Mohammed A.
dc.contributor.authorAlnaseeri, Mohanaad T
dc.contributor.authorMohammed, Alaa Hamid
dc.contributor.authorSahib, Haneen Ali
dc.date.accessioned2021-05-15T12:49:46Z
dc.date.available2021-05-15T12:49:46Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Elektirk ve Bilgisayar Mühendisliği Bölümüen_US
dc.description2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2020 -- 26 June 2020 through 27 June 2020 -- -- 162106
dc.description.abstractThe shape is essential to image formats where the textual image is the best example of the binary image representation. Feature extraction is a fundamental process in pattern recognition. the shape and texture recognition system consists of five major tasks which are involved pre-processing, segmentation, feature extraction, classification and recognition. GENERALLY, less discriminative features in global and local feature approach leads to reduce in recognition rate. By proposing a global and local approach that produces more discriminative features and less dimensionality of data, these problems are overcome. Two feature extraction methods are studied namely Gabor filter and edge direction matrix (EDMS) and combination of two popular feature extraction methods is proposed The proposed method is a combination of Gabor filter and EDMS method which applied to reduce the dimensionality of data. this collaboration aims to make use of the major advantages each one presents, by simultaneously complementing each other, in order to elevate their weak points. By using classifier approaches such as random forest, the proposed combinative descriptor is compared with the state of the art combinative methods based on Gray-Level Co-occurrence matrix and moment invariant on two benchmark dataset MPEG-7 CE-Shape-1, Enghlishfnt. The experiments have shown the superiority of the introduced descriptor over the GLCM moment invariants from the literature. © 2020 IEEE.en_US
dc.identifier.doi10.1109/HORA49412.2020.9152829
dc.identifier.isbn9781728193526
dc.identifier.scopus2-s2.0-85089674308
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/HORA49412.2020.9152829
dc.identifier.urihttps://hdl.handle.net/20.500.12939/1115
dc.identifier.wosWOS:000644404300110
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorUçan, Osman Nuri
dc.institutionauthorSahib, Haneen Ali
dc.institutionauthorSahib, Zubaidah Ali
dc.institutionauthorMohammed, Alaa Hamid
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofHORA 2020 - 2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectEdge Direction Matrix (EDMS)en_US
dc.subjectFeature Extractionen_US
dc.subjectGabor Filteren_US
dc.subjectPattern Recognitionen_US
dc.titleHybrid method using EDMS gabor for shape and texture
dc.typeConference Object

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Bilgisayar Mühendisliği Bölümü Koleksiyonu
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