Performance Comparison of Texture Classifiers on Small Windows

Baykal, Ibrahim Cem

doi:10.1109/idap.2019.8875882

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Performance Comparison On Small Windows1

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2023

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“…If the image of the texture is high resolution, than these window sizes must be increased. More detailed results of this experiment were shared in a conference article [19]. www.ijacsa.thesai.org IV.…”

Section: Performance Comparison On Small Windowsmentioning

confidence: 99%

Frequency Domain Improvements to Texture Discrimination Algorithms

Baykal¹

2023

IJACSA

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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.

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Section: Performance Comparison On Small Windowsmentioning

confidence: 99%