Research Article
BibTex RIS Cite

Automatic Inspection of the Warp-Weft Density Using Image Processing Techniques

Year 2016, Volume: 23 Issue: 104, 247 - 262, 30.12.2016
https://doi.org/10.7216/1300759920162310402

Abstract

In this study, possibility of determining warp and weft yarn density of colored and figured plain and twill woven fabrics by Wiener filter, median filter, grey level co-occurrence matrix and gray line profile methods, which are spatial techniques, and by Fourier and wavelet transformation methods, which are frequency domain techniques, are investigated. Considering the spatial techniques, the most successful technique that determines warp and weft densities of plain and twill fabrics is the median filter method. The following successful techniques are Wiener filter and gray level co-occurrence matrix. On the other hand, it is obtained that Fourier analysis method, one of the frequency domain techniques, which depends on the counting of the harmonics of the yarns, provide more successful result than spatial techniques. 

References

  • Lin, J. J. (2002), Applying a Co-occurrence Matrix to Automatic Inspection of Weaving Density for Woven Fabrics, Textile Research Journal, 72, 486-490.
  • Techniková, L. ve Tunák, M. (2013), Weaving Density Evaluation with the Aid of Image Analysis, Fibres & Textiles in Eastern Europe, 21(2), 74-79.
  • Sari-sarraf, H. (1996), On-line Optical Measurement and Monitoring of Yarn Density in Woven Fabrics, Photonics China '96 Symposium on Automated Optical Inspection for Industry: Theory, Technology, and Application. Beijing, China, 444-452.
  • Maroš, T. ve Aleš, L. (2004), Applying Spectral Analysis to Automatic Inspection of Weaving Density, 16th International Conference Structure and Structural Mechanics of Textiles. Liberec, Çek Cumhuriyeti.
  • Pan R., Gao W., Li Z., Gou J., Zhang J., Zhu D. (2015), Measuring Thread Densities of Woven Fabric Using the Fourier Transform, FIBRES & TEXTILES in Eastern Europe, 23, 1(109), 35-40.
  • Lachkar, A., Gadi T., Benslimane, R., D'Orazio, L., Martuscelli, E. (2003), Textile Woven-fabric Recognition by Using Fourier Image-analysis Techniques: Part I: A Fully Automatic Approach for Crossedpoints Detection, The Journal of The Textile Institute, 94(3-4), 194-201.
  • Pan, R., Gao W., Liu, J., Wang, H., Qian, X. (2011), Automatic Inspection of Double-system-mélange Yarn-dyed Fabric Density with Color-gradient Image, Fibers and Polymers, 12(1), 127-131.
  • Li, L.Q., Chen, X. ve Huang, X.B. (2005), Automatic Inspection of Weaving Density for Woven Fabrics Using Adaptive Wavelets, Journal of Donghua University, 31, 63-65
  • Chan, C. Pang, G. (2000), Fabric Defect Detection by Fourier Analysis, IEEE Transactions on Industry Applications, 36(5), 1267-1276.
  • Lachkar, A., Benslimane, R., D'Orazio, L., Martuscelli, E.(2005) Textile Woven Fabric Recognition Using Fourier Image Analysis Techniques: Part II – Texture Analysis for Crossed-States Detection, The Journal of The Textile Institute, 96(3), 179-183.
  • Feng, Y.L. ve Li, R.Q. (2001), Automatic Measurement of Weave Count with Wavelet Transfer, Journal of Textile Research, 22, 30-31.
  • Jing, J., Liu S. (2014) Automatic Density Detection of Woven Fabrics via Wavelet Transform, Journal of Information and Computational Science, 11(8), 2559-2568.
  • Yili F., et al (2001), Automatic Measurement of Weave Count with Wavelet Transfer, Journal of Textile Research, 2001-02. 22(2), 94-95.
  • Pan, R., Gao, W., Liu, J., Wang, H. ve Zhang, X. (2010), Automatic Detection of Structure Parameters of Yarn-dyed Fabric, Textile Research Journal, 80(17), 1819-1932.
  • Pan, R., Gao, W., Liu, J. ve Wang, H. (2010), Automatic Inspection of Woven Fabric Density of Solid Colour Fabric Density by the Hough Transform, FIBRES & TEXTILES in Eastern Europe, 18(4), 46-51.
  • Yıldırım, B. ve Baser, G. (2009), Image Processing Approach for Weft Density Measurement on the Loom,16th International Conference Structure and Structural Mechanics of Textiles. 1-6, Aralık 2009, Liberec, Çek Cumhuriyeti.
  • Zhang, J., Xin2, B., Wu X. (2013), Review of Fabric Identification Based on Image Analysis Technology, Textiles and Light Industrial Science and Technology (TLIST), 2(3), 120-130.
  • Pan, R.R. ve Gao, W.D. (2008), High-precision Identification of Woven Fabric Density Via Image Processing, Journal of Textile Research, 29, 128-131.
  • Xie, L.Q. ve Yu, W.D. (2008), Applied Technique of Automatic Measurement of Warp and Weft Densities in Fabrics: 1. Method of Measurement, Journal of Textile Research, 29, 26-30.
  • Ohta, K., Nonaka, Y. ve Miyawaki, F. (1995), Automatic Analyzing of a Weaving Design with the Spatial Frequency Components, Image Analysis Applications and Computer Graphics, 1024, 516-51.
  • Gao, W.D., Liu, J.H., Xu, B.J., Di, W. ve Xue, W. (2002), Automatic Identification of Warps Arrangement Parameters in Fabric, Cotton Textile Technology, 30, 31-34.
  • Shady, E., Qashqary, K., Hassan, M. ve Militky, J. (2012), Image Processing Based Method Evaluating Fabric Structure Characteristics, Fibres & Textiles in Eastern Europe, 20, 86- 90.
  • Jeong, Y., Jang J. (2005), Applying Image Analysis to Automatic Inspection of Fabric Density for Woven Fabrics, Fibers and Polymers, 6(2), 156-161.
  • Yıldırım B. (2013), Determination of Optimum Filter Size for Detecting Yarn Boundaries, Fibers and Polymers, 14(10), 1739-1747.
  • Lim, Jae S. (1990), Two-Dimensional Signal and Image Processing, Prentice Hall, Englewood Cliffs, New Jersey.
  • Raheja, L., J., Ajay, B., Chaudhary (2013), A. Real Time Fabric Defect Detection System on an Embedded DSP Platform, Optik: International Journal for Light and Electron Optics, Elsevier, 124(21), 5280-5284.
  • Zhou, J., Li, G., Wan X., Wang F. (2015), A Real-Time Computer Vision-Based Platform for Fabric Inspection Part 2: Platform Design and Real Time Implementation, The Journal of The Textile Institute, 107(2), 264-272.

Kumaş Sıklıklarının Görüntü İşleme Teknikleri ile Otomatik Olarak Belirlenmesi

Year 2016, Volume: 23 Issue: 104, 247 - 262, 30.12.2016
https://doi.org/10.7216/1300759920162310402

Abstract

Bu çalışmada, uzamsal yöntemler olan Wiener filtre, medyan filtre, gri düzeyli eş-oluşum matrisi, gri sıra kesit teknikleri ile, frekans uzayı yöntemlerinden Fourier ve dalgacık dönüşümü teknikleri kullanılarak renkli ve desenli bezayağı ve dimi örgülü kumaşların çözgü ve atkı sıklıklarının belirlenme olanakları araştırılmıştır. Uzamsal yöntemler açısından, bezayağı ve dimi örgülü kumaşların çözgü ve atkı sıklıklarının belirlenmesinde en başarılı yöntem medyan filtre yöntemidir. Medyan filtre yöntemini, Wiener filtre ve gri düzeyli eş-oluşum matris yöntemleri izlemektedir. Diğer yandan, frekans uzayı yöntemlerinden Fourier analizi yöntemi, kumaş görüntülerinde bulunan örüntülerin frekans uzayındaki harmoniklerinin tespiti esasına dayanmakta ve uzamsal yöntemlere oranla daha yüksek başarım oranı elde etmektedir. 

References

  • Lin, J. J. (2002), Applying a Co-occurrence Matrix to Automatic Inspection of Weaving Density for Woven Fabrics, Textile Research Journal, 72, 486-490.
  • Techniková, L. ve Tunák, M. (2013), Weaving Density Evaluation with the Aid of Image Analysis, Fibres & Textiles in Eastern Europe, 21(2), 74-79.
  • Sari-sarraf, H. (1996), On-line Optical Measurement and Monitoring of Yarn Density in Woven Fabrics, Photonics China '96 Symposium on Automated Optical Inspection for Industry: Theory, Technology, and Application. Beijing, China, 444-452.
  • Maroš, T. ve Aleš, L. (2004), Applying Spectral Analysis to Automatic Inspection of Weaving Density, 16th International Conference Structure and Structural Mechanics of Textiles. Liberec, Çek Cumhuriyeti.
  • Pan R., Gao W., Li Z., Gou J., Zhang J., Zhu D. (2015), Measuring Thread Densities of Woven Fabric Using the Fourier Transform, FIBRES & TEXTILES in Eastern Europe, 23, 1(109), 35-40.
  • Lachkar, A., Gadi T., Benslimane, R., D'Orazio, L., Martuscelli, E. (2003), Textile Woven-fabric Recognition by Using Fourier Image-analysis Techniques: Part I: A Fully Automatic Approach for Crossedpoints Detection, The Journal of The Textile Institute, 94(3-4), 194-201.
  • Pan, R., Gao W., Liu, J., Wang, H., Qian, X. (2011), Automatic Inspection of Double-system-mélange Yarn-dyed Fabric Density with Color-gradient Image, Fibers and Polymers, 12(1), 127-131.
  • Li, L.Q., Chen, X. ve Huang, X.B. (2005), Automatic Inspection of Weaving Density for Woven Fabrics Using Adaptive Wavelets, Journal of Donghua University, 31, 63-65
  • Chan, C. Pang, G. (2000), Fabric Defect Detection by Fourier Analysis, IEEE Transactions on Industry Applications, 36(5), 1267-1276.
  • Lachkar, A., Benslimane, R., D'Orazio, L., Martuscelli, E.(2005) Textile Woven Fabric Recognition Using Fourier Image Analysis Techniques: Part II – Texture Analysis for Crossed-States Detection, The Journal of The Textile Institute, 96(3), 179-183.
  • Feng, Y.L. ve Li, R.Q. (2001), Automatic Measurement of Weave Count with Wavelet Transfer, Journal of Textile Research, 22, 30-31.
  • Jing, J., Liu S. (2014) Automatic Density Detection of Woven Fabrics via Wavelet Transform, Journal of Information and Computational Science, 11(8), 2559-2568.
  • Yili F., et al (2001), Automatic Measurement of Weave Count with Wavelet Transfer, Journal of Textile Research, 2001-02. 22(2), 94-95.
  • Pan, R., Gao, W., Liu, J., Wang, H. ve Zhang, X. (2010), Automatic Detection of Structure Parameters of Yarn-dyed Fabric, Textile Research Journal, 80(17), 1819-1932.
  • Pan, R., Gao, W., Liu, J. ve Wang, H. (2010), Automatic Inspection of Woven Fabric Density of Solid Colour Fabric Density by the Hough Transform, FIBRES & TEXTILES in Eastern Europe, 18(4), 46-51.
  • Yıldırım, B. ve Baser, G. (2009), Image Processing Approach for Weft Density Measurement on the Loom,16th International Conference Structure and Structural Mechanics of Textiles. 1-6, Aralık 2009, Liberec, Çek Cumhuriyeti.
  • Zhang, J., Xin2, B., Wu X. (2013), Review of Fabric Identification Based on Image Analysis Technology, Textiles and Light Industrial Science and Technology (TLIST), 2(3), 120-130.
  • Pan, R.R. ve Gao, W.D. (2008), High-precision Identification of Woven Fabric Density Via Image Processing, Journal of Textile Research, 29, 128-131.
  • Xie, L.Q. ve Yu, W.D. (2008), Applied Technique of Automatic Measurement of Warp and Weft Densities in Fabrics: 1. Method of Measurement, Journal of Textile Research, 29, 26-30.
  • Ohta, K., Nonaka, Y. ve Miyawaki, F. (1995), Automatic Analyzing of a Weaving Design with the Spatial Frequency Components, Image Analysis Applications and Computer Graphics, 1024, 516-51.
  • Gao, W.D., Liu, J.H., Xu, B.J., Di, W. ve Xue, W. (2002), Automatic Identification of Warps Arrangement Parameters in Fabric, Cotton Textile Technology, 30, 31-34.
  • Shady, E., Qashqary, K., Hassan, M. ve Militky, J. (2012), Image Processing Based Method Evaluating Fabric Structure Characteristics, Fibres & Textiles in Eastern Europe, 20, 86- 90.
  • Jeong, Y., Jang J. (2005), Applying Image Analysis to Automatic Inspection of Fabric Density for Woven Fabrics, Fibers and Polymers, 6(2), 156-161.
  • Yıldırım B. (2013), Determination of Optimum Filter Size for Detecting Yarn Boundaries, Fibers and Polymers, 14(10), 1739-1747.
  • Lim, Jae S. (1990), Two-Dimensional Signal and Image Processing, Prentice Hall, Englewood Cliffs, New Jersey.
  • Raheja, L., J., Ajay, B., Chaudhary (2013), A. Real Time Fabric Defect Detection System on an Embedded DSP Platform, Optik: International Journal for Light and Electron Optics, Elsevier, 124(21), 5280-5284.
  • Zhou, J., Li, G., Wan X., Wang F. (2015), A Real-Time Computer Vision-Based Platform for Fabric Inspection Part 2: Platform Design and Real Time Implementation, The Journal of The Textile Institute, 107(2), 264-272.
There are 27 citations in total.

Details

Subjects Engineering
Journal Section Articles
Authors

Erdoğan Aldemir This is me

Hakan Özdemir

Selçuk Kılınç

Publication Date December 30, 2016
Published in Issue Year 2016 Volume: 23 Issue: 104

Cite

APA Aldemir, E., Özdemir, H., & Kılınç, S. (2016). Kumaş Sıklıklarının Görüntü İşleme Teknikleri ile Otomatik Olarak Belirlenmesi. Tekstil Ve Mühendis, 23(104), 247-262. https://doi.org/10.7216/1300759920162310402
AMA Aldemir E, Özdemir H, Kılınç S. Kumaş Sıklıklarının Görüntü İşleme Teknikleri ile Otomatik Olarak Belirlenmesi. Tekstil ve Mühendis. December 2016;23(104):247-262. doi:10.7216/1300759920162310402
Chicago Aldemir, Erdoğan, Hakan Özdemir, and Selçuk Kılınç. “Kumaş Sıklıklarının Görüntü İşleme Teknikleri Ile Otomatik Olarak Belirlenmesi”. Tekstil Ve Mühendis 23, no. 104 (December 2016): 247-62. https://doi.org/10.7216/1300759920162310402.
EndNote Aldemir E, Özdemir H, Kılınç S (December 1, 2016) Kumaş Sıklıklarının Görüntü İşleme Teknikleri ile Otomatik Olarak Belirlenmesi. Tekstil ve Mühendis 23 104 247–262.
IEEE E. Aldemir, H. Özdemir, and S. Kılınç, “Kumaş Sıklıklarının Görüntü İşleme Teknikleri ile Otomatik Olarak Belirlenmesi”, Tekstil ve Mühendis, vol. 23, no. 104, pp. 247–262, 2016, doi: 10.7216/1300759920162310402.
ISNAD Aldemir, Erdoğan et al. “Kumaş Sıklıklarının Görüntü İşleme Teknikleri Ile Otomatik Olarak Belirlenmesi”. Tekstil ve Mühendis 23/104 (December 2016), 247-262. https://doi.org/10.7216/1300759920162310402.
JAMA Aldemir E, Özdemir H, Kılınç S. Kumaş Sıklıklarının Görüntü İşleme Teknikleri ile Otomatik Olarak Belirlenmesi. Tekstil ve Mühendis. 2016;23:247–262.
MLA Aldemir, Erdoğan et al. “Kumaş Sıklıklarının Görüntü İşleme Teknikleri Ile Otomatik Olarak Belirlenmesi”. Tekstil Ve Mühendis, vol. 23, no. 104, 2016, pp. 247-62, doi:10.7216/1300759920162310402.
Vancouver Aldemir E, Özdemir H, Kılınç S. Kumaş Sıklıklarının Görüntü İşleme Teknikleri ile Otomatik Olarak Belirlenmesi. Tekstil ve Mühendis. 2016;23(104):247-62.