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DOI: 10.14569/IJACSA.2023.0140324
PDF

Frequency Domain Improvements to Texture Discrimination Algorithms

Author 1: Ibrahim Cem Baykal

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 3, 2023.

  • Abstract and Keywords
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Abstract: 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.

Keywords: Machine vision; ANN; SVM; pattern recognition; co-occurrence; texture feature extraction

Ibrahim Cem Baykal, “Frequency Domain Improvements to Texture Discrimination Algorithms” International Journal of Advanced Computer Science and Applications(IJACSA), 14(3), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140324

@article{Baykal2023,
title = {Frequency Domain Improvements to Texture Discrimination Algorithms},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140324},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140324},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Ibrahim Cem Baykal}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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