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Year 2021, Volume: 31 Issue: 2, 91 - 98, 30.06.2021
https://doi.org/10.32710/tekstilvekonfeksiyon.770366

Abstract

References

  • Behera, B. K. (2004). Image-processing in textiles. Textile Progress, 35(2-4), 1-193.
  • Wong, C. (Ed.). (2017). Applications of Computer Vision in Fashion and Textiles. Woodhead Publishing.
  • Haleem, N., & Wang, X. (2013). A comparative study on yarn hairiness results from manual test and two commercial hairiness metres. Journal of The Textile Institute, 104(5), 494-501.
  • Zhang, G., & Xin, B. (2016). An overview of the application of image processing technology for yarn hairiness evaluation. Research Journal of Textile and Apparel. 20(1), 24-36.
  • Haleem, N., & Wang, X. (2015). Recent research and developments on yarn hairiness. Textile Research Journal, 85(2), 211-224.
  • Carvalho, V., Cardoso, P., Belsley, M., Vasconcelos, R. M., & Soares, F. O. (2008). Yarn hairiness parameterization using a coherent signal processing technique. Sensors and Actuators A: Physical, 142(1), 217-224.
  • Ozkaya, Y. A., Acar, M., & Jackson, M. R. (2008). Simulation of photosensor-based hairiness measurement using digital image analysis. The Journal of The Textile Institute, 99(2), 93-100.
  • Jackson, M., Acar, M., Yuen, S. L., & Whitby, D. (1995). A vision based yarn scanning system. Mechatronics, 5(2-3), 133-146.
  • Cybulska, M. (1999). Assessing yarn structure with image analysis methods1. Textile Research Journal, 69(5), 369-373.
  • Kuzanski, M. (2006, May). Measurement Methods for Yarn Hairiness Analysis-the idea and construction of research standing. In Proceedings of the 2nd international conference on perspective technologies and methods in MEMS design (pp. 87-90). IEEE.
  • Kuzanski, M., & Jackowska-Strumillo, L. (2007, May). Yarn hairiness determination the algorithms of computer measurement methods. In 2007 International Conference on Perspective Technologies and Methods in MEMS Design (pp. 155-158). IEEE.
  • Fabijanska, A., Kuzanski, M., Sankowski, D., & Jackowska-Strumillo, L. (2008, May). Application of image processing and analysis in selected industrial computer vision systems. In 2008 International Conference on Perspective Technologies and Methods in MEMS Design (pp. 27-31). IEEE.
  • Fabijańska, A. (2010, April). A survey of thresholding algorithms on yarn images. In 2010 Proceedings of VIth International Conference on Perspective Technologies and Methods in MEMS Design (pp. 23-26). IEEE.
  • Fabijańska, A. (2011). Yarn image segmentation using the region growing algorithm. Measurement Science and Technology, 22(11), 114024.
  • Fabijańska, A., & Jackowska-Strumiłło, L. (2012). Image processing and analysis algorithms for yarn hairiness determination. Machine Vision and Applications, 23(3), 527-540.
  • Guha, A., Amarnath, C., Pateria, S., & Mittal, R. (2010). Measurement of yarn hairiness by digital image processing. The journal of the Textile Institute, 101(3), 214-222.
  • Roy, S., Sengupta, A., & Sengupta, S. (2014). Yarn hairiness evaluation using image processing. In Proceedings of The 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC) (pp. 588-592). IEEE.
  • Wang, R., Zhou, J., Yu, L., & Xu, B. (2014). Fusing multifocus images for yarn hairiness measurement. Optical Engineering, 53(12), 123101.
  • Goncalves, N., Carvalho, V., Belsley, M., Vasconcelos, R. M., Soares, F. O., & Machado, J. (2015). Yarn features extraction using image processing and computer vision–A study with cotton and polyester yarns. Measurement, 68, 1-15.
  • Sengupta, A., Roy, S., & Sengupta, S. (2015). Development of a low cost yarn parameterisation unit by image processing. Measurement, 59, 96-109.
  • Telli, A. (2019). An Image Processing Research Consistent with Standard Photographs to Determine Pilling Grade of Woven Fabrics. Tekstil ve Konfeksiyon, 29(3), 268-276.
  • Mathworks, Find edges in intensity image, Retrieved from https://www.mathworks.com/help/images/ref/edge.html?s_tid=srchtitle
  • Mathworks, Average or mean of matrix elements, Retrieved from https://www.mathworks.com/help/images/ref/mean2.html?searchHighlight=mean%20of%20matrix%20elements&s_tid=doc_srchtitle
  • Mathworks, Standard deviation of matrix elements, Retrieved from https://www.mathworks.com/help/images/ref/std2.html?searchHighlight=Standard%20deviation%20of%20matrix%20elements&s_tid=doc_srchtitle
  • Mathworks, Entropy of grayscale image, Retrieved from https://www.mathworks.com/help/images/ref/entropy.html?searchHighlight=The%20entropy%20of%20grayscale%20images&s_tid=doc_srchtitle
  • Mathworks, Texture Analysis Using the Gray-Level Co-Occurrence Matrix (GLCM), Retrieved from https://www.mathworks.com/help/images/texture-analysis-using-the-gray-level-co-occurrence-matrix-glcm.html?s_tid=srchtitle
  • Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6), 610-621.
  • Xin, B., Zhang, J., Zhang, R., & Wu, X. (2017). Color texture classification of yarn-dyed woven fabric based on dual-side scanning and co-occurrence matrix. Textile Research Journal, 87(15), 1883-1895.
  • Eldessouki, M., & Hassan, M. (2015). Adaptive neuro-fuzzy system for quantitative evaluation of woven fabrics’ pilling resistance. Expert Systems with Applications, 42(4), 2098-2113.

The Comparison of the Edge Detection Methods in the Determination of Yarn Hairiness through Image Processing

Year 2021, Volume: 31 Issue: 2, 91 - 98, 30.06.2021
https://doi.org/10.32710/tekstilvekonfeksiyon.770366

Abstract

In this study, an image processing approach for the determination of yarn hairiness was presented. Yarn images taken under microscope were examined in MATLAB software. Seven different edge detection algorithms were used in order to separate the hairs from the yarn body accurately. Seven different textural properties of obtained yarn images were compared with Zweigle hairiness test results. The best hairiness results were obtained in Sobel and Prewitt edge detection methods. The findings have indicated that there were stronger correlation values for Sobel and Prewitt methods between Zweigle indices and four different textural features.

References

  • Behera, B. K. (2004). Image-processing in textiles. Textile Progress, 35(2-4), 1-193.
  • Wong, C. (Ed.). (2017). Applications of Computer Vision in Fashion and Textiles. Woodhead Publishing.
  • Haleem, N., & Wang, X. (2013). A comparative study on yarn hairiness results from manual test and two commercial hairiness metres. Journal of The Textile Institute, 104(5), 494-501.
  • Zhang, G., & Xin, B. (2016). An overview of the application of image processing technology for yarn hairiness evaluation. Research Journal of Textile and Apparel. 20(1), 24-36.
  • Haleem, N., & Wang, X. (2015). Recent research and developments on yarn hairiness. Textile Research Journal, 85(2), 211-224.
  • Carvalho, V., Cardoso, P., Belsley, M., Vasconcelos, R. M., & Soares, F. O. (2008). Yarn hairiness parameterization using a coherent signal processing technique. Sensors and Actuators A: Physical, 142(1), 217-224.
  • Ozkaya, Y. A., Acar, M., & Jackson, M. R. (2008). Simulation of photosensor-based hairiness measurement using digital image analysis. The Journal of The Textile Institute, 99(2), 93-100.
  • Jackson, M., Acar, M., Yuen, S. L., & Whitby, D. (1995). A vision based yarn scanning system. Mechatronics, 5(2-3), 133-146.
  • Cybulska, M. (1999). Assessing yarn structure with image analysis methods1. Textile Research Journal, 69(5), 369-373.
  • Kuzanski, M. (2006, May). Measurement Methods for Yarn Hairiness Analysis-the idea and construction of research standing. In Proceedings of the 2nd international conference on perspective technologies and methods in MEMS design (pp. 87-90). IEEE.
  • Kuzanski, M., & Jackowska-Strumillo, L. (2007, May). Yarn hairiness determination the algorithms of computer measurement methods. In 2007 International Conference on Perspective Technologies and Methods in MEMS Design (pp. 155-158). IEEE.
  • Fabijanska, A., Kuzanski, M., Sankowski, D., & Jackowska-Strumillo, L. (2008, May). Application of image processing and analysis in selected industrial computer vision systems. In 2008 International Conference on Perspective Technologies and Methods in MEMS Design (pp. 27-31). IEEE.
  • Fabijańska, A. (2010, April). A survey of thresholding algorithms on yarn images. In 2010 Proceedings of VIth International Conference on Perspective Technologies and Methods in MEMS Design (pp. 23-26). IEEE.
  • Fabijańska, A. (2011). Yarn image segmentation using the region growing algorithm. Measurement Science and Technology, 22(11), 114024.
  • Fabijańska, A., & Jackowska-Strumiłło, L. (2012). Image processing and analysis algorithms for yarn hairiness determination. Machine Vision and Applications, 23(3), 527-540.
  • Guha, A., Amarnath, C., Pateria, S., & Mittal, R. (2010). Measurement of yarn hairiness by digital image processing. The journal of the Textile Institute, 101(3), 214-222.
  • Roy, S., Sengupta, A., & Sengupta, S. (2014). Yarn hairiness evaluation using image processing. In Proceedings of The 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC) (pp. 588-592). IEEE.
  • Wang, R., Zhou, J., Yu, L., & Xu, B. (2014). Fusing multifocus images for yarn hairiness measurement. Optical Engineering, 53(12), 123101.
  • Goncalves, N., Carvalho, V., Belsley, M., Vasconcelos, R. M., Soares, F. O., & Machado, J. (2015). Yarn features extraction using image processing and computer vision–A study with cotton and polyester yarns. Measurement, 68, 1-15.
  • Sengupta, A., Roy, S., & Sengupta, S. (2015). Development of a low cost yarn parameterisation unit by image processing. Measurement, 59, 96-109.
  • Telli, A. (2019). An Image Processing Research Consistent with Standard Photographs to Determine Pilling Grade of Woven Fabrics. Tekstil ve Konfeksiyon, 29(3), 268-276.
  • Mathworks, Find edges in intensity image, Retrieved from https://www.mathworks.com/help/images/ref/edge.html?s_tid=srchtitle
  • Mathworks, Average or mean of matrix elements, Retrieved from https://www.mathworks.com/help/images/ref/mean2.html?searchHighlight=mean%20of%20matrix%20elements&s_tid=doc_srchtitle
  • Mathworks, Standard deviation of matrix elements, Retrieved from https://www.mathworks.com/help/images/ref/std2.html?searchHighlight=Standard%20deviation%20of%20matrix%20elements&s_tid=doc_srchtitle
  • Mathworks, Entropy of grayscale image, Retrieved from https://www.mathworks.com/help/images/ref/entropy.html?searchHighlight=The%20entropy%20of%20grayscale%20images&s_tid=doc_srchtitle
  • Mathworks, Texture Analysis Using the Gray-Level Co-Occurrence Matrix (GLCM), Retrieved from https://www.mathworks.com/help/images/texture-analysis-using-the-gray-level-co-occurrence-matrix-glcm.html?s_tid=srchtitle
  • Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6), 610-621.
  • Xin, B., Zhang, J., Zhang, R., & Wu, X. (2017). Color texture classification of yarn-dyed woven fabric based on dual-side scanning and co-occurrence matrix. Textile Research Journal, 87(15), 1883-1895.
  • Eldessouki, M., & Hassan, M. (2015). Adaptive neuro-fuzzy system for quantitative evaluation of woven fabrics’ pilling resistance. Expert Systems with Applications, 42(4), 2098-2113.
There are 29 citations in total.

Details

Primary Language English
Subjects Wearable Materials
Journal Section Articles
Authors

Abdurrahman Telli 0000-0002-6720-9410

Publication Date June 30, 2021
Submission Date July 16, 2020
Acceptance Date June 30, 2021
Published in Issue Year 2021 Volume: 31 Issue: 2

Cite

APA Telli, A. (2021). The Comparison of the Edge Detection Methods in the Determination of Yarn Hairiness through Image Processing. Textile and Apparel, 31(2), 91-98. https://doi.org/10.32710/tekstilvekonfeksiyon.770366

No part of this journal may be reproduced, stored, transmitted or disseminated in any forms or by any means without prior written permission of the Editorial Board. The views and opinions expressed here in the articles are those of the authors and are not the views of Tekstil ve Konfeksiyon and Textile and Apparel Research-Application Center.