Diftogram: A Better Texture Discriminator

Basit Öğe Kaydı
dc.contributor.authorBaykal, Ibrahim Cem
dc.date.accessioned2025-01-06T17:29:43Z
dc.date.available2025-01-06T17:29:43Z
dc.date.issued2019
dc.description2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018 -- 28 September 2018 through 30 September 2018 -- Malatya -- 144523
dc.description.abstractThis article aims to design the most efficient texture discriminator by proposing two alterations to the existing algorithms. The first argument presented in this article is that the direct use of Unser's difference histogram (not sum) as input to a classifier yields more successful results than Haralick's method. The second proposal is the introduction of two different methods for the selection of the pixel distance and the orientation. Both mathematical and empirical evidence is presented, proving that these methods improve the texture discrimination rate by %250-%300 while reducing the computational complexity by %50. © 2018 IEEE.
dc.description.sponsorshipAdama Science and Technology University, ASTU, (16103010)
dc.identifier.doi10.1109/IDAP.2018.8620894
dc.identifier.isbn978-153866878-8
dc.identifier.scopus2-s2.0-85062488214
dc.identifier.urihttps://doi.org/10.1109/IDAP.2018.8620894
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1311
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectImage processing
dc.subjectlinear discriminant analysis
dc.subjectneural networks
dc.subjectpattern recognition
dc.subjecttexture feature extraction
dc.titleDiftogram: A Better Texture Discriminator
dc.typeConference Object

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu