Frequency Domain Improvements to Texture Discrimination Algorithms
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Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Science and Information Organization
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
As the production speeds of factories increase, it becomes more and more challenging to inspect products in real time. The goal of this article is to come up with a computationally efficient texture discrimination algorithm by first testing their ability to localize defects and then increase their efficiency by removing less effective parts of them. Therefore, abilities of the most popular texture classification algorithms such as the GLCM, the LBP and the SDH to localize defects are tested on different datasets. These tests reveal that, on small windows GLCM and SDH perform better. Frequency properties of the textures are used to fine-tune the parameters of these algorithms. Further experiments on three different datasets prove that the accuracy of the algorithms are increased almost twice while decreasing the processing time considerably © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.
Açıklama
Anahtar Kelimeler
ANN, co-occurrence, Machine vision, pattern recognition, SVM, texture feature extraction
Kaynak
International Journal of Advanced Computer Science and Applications
WoS Q Değeri
Scopus Q Değeri
Q3
Cilt
14
Sayı
3
