Measuring Thread Densities of Woven Fabric Using the Fourier Transform

• Autorzy: Pan R. , Gao W. , Li Z. , Gou J. , Zhang J. , Zhu D.

Warianty tytułu

PL

Pomiar gestości nitek w tkaninach przy zastosowaniu transformaty Fouriera

• Języki publikacji: EN

Abstrakty

EN

To replace time-consuming and lab-intensive of manual inspection, a Fourier transform is proposed to detect the thread densities of woven fabric in this paper. First, theories of the Fourier transform, yarn image reconstruction and the threshold method are introduced. Then the steps of fabric image acquisition, the Fourier transform of the fabric image, feature analysis in the frequency domain, image construction of fabric yarns, and threshold processing are analysed. Lastly, after locating and counting the yarns in the fabric segmentation results, thread densities of the woven fabric are calculated. The experimental results prove that the method proposed can detect the thread densities of woven fabric correctly and it can be used to replace the current manual analysis.

PL

W celu zastąpienia pracochłonnego i czasochłonnego ręcznego sprawdzania zaproponowano specjalny system dla oznaczania gęstości nitek w tkaninie. Przedstawiono teorię transformacji Fouriera, rekonstrukcję wyglądu przędzy wraz z metodą progową. Określono stopnie akwizycji obrazu tkaniny, zastosowanie transformacji Fouriera, analizę częstotliwościową właściwości, konstrukcję obrazu nitek tkaniny i zastosowanie metody progowej. Pod koniec, po lokalizacji i zliczeniu nitek obliczono gęstość nitek w tkaninie. Wyniki pomiarów potwierdzają, że zaproponowana metoda automatycznej detekcji nitek jest prawidłowa.

Słowa kluczowe

EN

thread density; woven fabric; Fourier transform; threshold segmentation; image reconstruction

PL

gęstość nici w tkaninie; transformacja Fouriera; próg segmentacji; rekonstrukcja obrazu

• Wydawca: Sieć Badawcza Łukasiewicz - Łódzki Instytut Technologiczny
• Czasopismo: Fibres & Textiles in Eastern Europe
• Rocznik: 2015
• Tom: Vol. 23, no. 1 (109)
• Strony: 35--40
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Pan R.

• School of Textile & Clothing, Jiangnan University, WuXi, P. R. China

autor

Gao W.

• School of Textile & Clothing, Jiangnan University, WuXi, P. R. China

autor

Li Z.

• School of Textile & Clothing, Jiangnan University, WuXi, P. R. China

autor

Gou J.

• School of Textile & Clothing, Jiangnan University, WuXi, P. R. China

autor

Zhang J.

• School of Textile & Clothing, Jiangnan University, WuXi, P. R. China

autor

Zhu D.

• School of Textile & Clothing, Jiangnan University, WuXi, P. R. China

Bibliografia

• 1. Yu X, Xin B, Gerrge B, Hu J. Fourieranalysis based satin fabric density and weaving pattern extraction. Research Journal of Textile and Apparel 2007;11, 1: 71-80.
• 2. Escofet J, Millán M, Ralló M. Modeling of woven fabric structures based on Fourier image analysis. Journal of Applied Optics 2001; 40, 34: 6171-6176.
• 3. Liu J, Jiang H, Pan R, Gao W, Xu M. Evaluation of yarn evenness in fabric based on image processing. Textile Research Journal 2012; 82, 10: 1026-1037.
• 4. Ralló M, Escofet J, Millán M. Weave-repeat identification by structural analysis of fabric images. Journal of Applied Optics 2003; 42, 17: 3361-3372.
• 5. Potiyaraj P, Subhakalin C, Sawangharsub B, Udomkichdecha, W. Recognition and re-visualization of woven fabric structures. International Journal of Clothing Science and Technology 2010; 22, 2-3: 79-87.
• 6. Sun Y, Chen X, Wang X. Automatic recognition of the density of woven fabrics. J. Donghua Univ. Natural Science Edition) 2006; 32, 2: 83-88.
• 7. Mourssa A, Dupont D, Steen D, Zeng X. Structure analysis and surface simulation of woven fabrics using fast Fourier transform techniques. Journal of the Textile Institute 2010; 101, 6: 556-570.
• 8. Xu B. Identifying fabric structure with fast Fourier transform techniques. Text. Res. J. 1996; 66, 8: 496-506.
• 9. Lachkar A, Gadi T, Benslimane R, D’Oraziob L, Martuscellib E. Textile woven-fabric recognition by using Fourier image-analysis techniques. Part I: A fully automatic approach for crossed- points detection. J. Text. Inst. 2003; 94, 3: 194-201.
• 10. Tunák M, Linka A, Volf P. Automatic assessing and monitoring of weaving density. FiberPolym. 2009; 10, 6: 830-836.
• 11. Li L, Chen X, Huang X. Automatic inspection of weaving density for woven fabrics using adaptive wavelets. J. Donghua Univ. Natural Science Edition 2005; 31, 1: 63-66.
• 12. He F, Li L, Xu J. Woven fabric density measure based on adaptive wavelets transform. J. Text. Res. 2007; 28, 2: 32-35.
• 13. Feng Y, Li L. Automatic measurement of weave count with wavelet transfer. J. Text. Res. 2001; 22, 2: 94-95.
• 14. Pan R, Gao W, Liu J, Wang H. Automatic inspection of woven fabric density of solid colour fabric density by the Hough transform. Fibres & Textiles in Eastern Europe2010; 18, 4: 46-51.
• 15. Pan R, Gao W, Liu J, Wang H. Automatic recognition of woven fabric pattern based on image processing and BP neural network. Journal of the Textile Institute 2011; 102, 1:19-30.
• 16 Pan R, Gao W, Liu J, Wang H, Qian, X. Automatic inspection of double-system- melange yarn-dyed fabric density with color-gradient image. Fibers and Polymers, 2011, 12(1): 127-131.
• 17. Lim J, Kim SM. Analysis of woven fabric structure using image analysis and artificial intelligence. Fibers and Polymers 2011; 12, 8: 1062-1068.
• 18. Kuo, CFJ, Shih CY, Ho CE, Peng KC. Application of computer vision in the automatic identification and classification of woven fabric weave patterns. Textile Research Journal 2010; 80, 20: 2144-2157.
• 19. Jeong YJ, Jang J. Applying image analysis to automatic inspection of fabric density for woven fabrics. Fibers and Polymers 2005; 26, 2: 156-161.
• 20. Kang TJ, Chang HK, Kung WO. Automatic recognition of fabric weave patterns by digital image analysis. Textile Research Journal 1999; 69, 2: 77-83.
• 21. Lin JJ. Applying a co-occurrence matrix to automatic inspection of weaving density for woven fabrics. Textile Research Journal 2002; 72, 6: 486-490.
• 22. Niblack W. An introduction to digital image processing. Prentice Hall, 1986.
• 23. Zhang XY, Pan RR, Liu JH, Gao WD, Xu WB. Design Gabor filters in the frequency domain for unsupervised fabric defect detection. Industria Textila 2011; 62, 4: 177-182.