Please use this identifier to cite or link to this item: https://dspace.upt.ro/xmlui/handle/123456789/7085
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dc.contributor.authorKrishnan, S.Navaneetha-
dc.contributor.authorVadivel, P. Sundara-
dc.contributor.authorYuvaraj, D.-
dc.contributor.authorMathusudhanan, S.R.-
dc.date.accessioned2025-02-04T11:25:14Z-
dc.date.available2025-02-04T11:25:14Z-
dc.date.issued2019-
dc.identifier.citationKrishnan, S.Navaneetha; Vadivel,P. Sundara; Yuvaraj,D.; Mathusudhanan,S.R.: Novel feature extraction methods for effective texture image and data classifications. Timişoara: Editura Politehnica, 2019.en_US
dc.identifier.issn1582-4594-
dc.identifier.urihttps://dspace.upt.ro/xmlui/handle/123456789/7085-
dc.description.abstractFeature Extraction is a process of capturing visual content of images for indexing & retrieval. Texture is a primary property of natural images which is of much importance in the fields of computer vision and computer graphics. Texture study is a type of image analysis producing measurements of the texture. These measurements may be of low- level, such as statistics of local facade or a result of higher level processing, such as segmentation of an image into different regions or the class of the texture present in an image. Identifying the superficial qualities of texture in an image is an important. The proposed work provides novel feature extraction schemes for identifying texture categories. Three frameworks have been proposed for 2D gray level images for classifying the textures.First two frameworks are designed for classifying the textures of gray scale images. The third frame work is proposed for classifying colour images.en_US
dc.language.isoenen_US
dc.publisherTimișoara : Editura Politehnicaen_US
dc.relation.ispartofseriesJournal of Electrical Engineering;Vol 19 No 2-
dc.subjectDTCWT methoden_US
dc.subjectSVM classifieren_US
dc.subjectCCMen_US
dc.subjectGLCMen_US
dc.subjectKNN classifieren_US
dc.titleNovel feature extraction methods for effective texture image and data classifications [articol]en_US
dc.typeArticleen_US
Appears in Collections:Articole științifice/Scientific articles

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