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DOI: 10.14569/IJACSA.2022.0131153
PDF

Method for 1/f Fluctuation Component Extraction from Images and Its Application to Improve Kurume Kasuri Quality Estimation

Author 1: Jin Shimazoe
Author 2: Kohei Arai
Author 3: Mariko Oda
Author 4: Jewon Oh

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 11, 2022.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Method for 1/f fluctuation component extraction from images is proposed. As an application of the proposed method, Kurume Kasuri textile quality evaluation is also proposed. Frequency component analysis is used for 1/f fluctuation component extraction. Also, an attempt is conducted to discriminate the typical Kurume Kasuri textile quality, (1) Relatively smooth edge lines are included in the Kurume Kasuri textile patterns, (2) Relatively non-smooth edge lines are included in the patterns, (3) Between both of patterns (1) and (2) by using template matching method of FLANN of OpenCV. Through experiments, it is found that the proposed method does work for extraction of 1/f fluctuation component and also found that Kurume Kasuri textile quality can be done with the result of 1/f fluctuation component extraction.

Keywords: 1/f fluctuation component extraction; Kurume Kasuri textile quality; FLANN; OpenCV

Jin Shimazoe, Kohei Arai, Mariko Oda and Jewon Oh, “Method for 1/f Fluctuation Component Extraction from Images and Its Application to Improve Kurume Kasuri Quality Estimation” International Journal of Advanced Computer Science and Applications(IJACSA), 13(11), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131153

@article{Shimazoe2022,
title = {Method for 1/f Fluctuation Component Extraction from Images and Its Application to Improve Kurume Kasuri Quality Estimation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131153},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131153},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {11},
author = {Jin Shimazoe and Kohei Arai and Mariko Oda and Jewon Oh}
}



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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