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Research Articles

Yarn-dyed woven fabric density measurement method and system based on multi-directional illumination image fusion enhancement technology

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Pages 1489-1501 | Received 15 Jan 2019, Accepted 10 Dec 2019, Published online: 23 Dec 2019

References

  • Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836. doi:10.1080/01621459.1979.10481038
  • Deng, D., Wang, R., Wu, H., He, H., Li, Q., & Luo, X. (2018). Learning deep similarity models with focus ranking for fabric image retrieval. Image and Vision Computing, 70, 11–20. doi:10.1016/j.imavis.2017.12.005
  • Di, Z., Luoqing, Z., & Jun, S. (2013). A novel feedback error-correcting algorithm for automatic recognition of the color and weave pattern of yarn-dyed fabrics. Textile Research Journal, 83(16), 1673–1689. doi:10.1177/0040517513481866
  • Eldessouki, M., & Ibrahim, S. (2016). Chan-Vese segmentation model for faster and accurate evaluation of yarn packing density. Textile Research Journal, 86(2), 167–177. doi:10.1177/0040517514557314
  • Grassi, A. P., Frolov, V., & León, F. P. (2011). Information fusion to detect and classify pedestrians using invariant features. Information Fusion, 12(4), 284–292. doi:10.1016/j.inffus.2010.06.002
  • Jing, J., Dong, A., Li, P., & Zhang, K. (2017). Yarn-dyed fabric defect classification based on convolutional neural network. Optical Engineering, 56(09), 1. doi:10.1117/1.OE.56.9.093104
  • Jing, J., Liu, S., Zhang, L., & Li, P. (2014). Skew detection and yarns density calculation for woven fabric. Journal of Fiber Bioengineering and Informatics, 7(4), 615–625. doi:10.3993/jfbi12201414
  • Kang, T. J., Kim, C. H., & Oh, K. W. (1999). Automatic recognition of fabric weave patterns by digital image analysis. Textile Research Journal, 69(2), 77–83. doi:10.1177/004051759906900201
  • Kuo, C. F. J., Shih, C. Y., Kao, C. Y., & Lee, J. Y. (2005). Color and pattern analysis of printed fabric by an unsupervised clustering method. Textile Research Journal, 75(1), 9–12. doi:10.1177/004051750507500103
  • Li, Z., Fan, Y., Liu, W., Yu, Z., & Wang, F. (2017). Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection. Journal of Electronic Imaging, 26(1), 13022. doi:10.1117/1.JEI.26.1.013022
  • Li, L. Q., Shan, T. T., Xue, L., Wang, J., & Chen, X. (2015). Study on woven fabric texture based on fourier transform and gabor transform. Key Engineering Materials, 671, 369–377. doi:10.4028/www.scientific.net/KEM.671.369
  • Lin, J. J. (2002). Applying a co-occurrence matrix to automatic inspection of weaving density for woven fabrics. Textile Research Journal, 72(6), 486–490. doi:10.1177/004051750207200604
  • Liu, J., Jiang, H., Liu, X., & Chai, Z. (2014). Automatic measurement for dimensional changes of woven fabrics based on texture. Measurement Science and Technology, 25(1), 15602. doi:10.1088/0957-0233/25/1/015602
  • Malek, A. S., Drean, J. Y., Bigue, L., & Osselin, J. F. (2013). Optimization of automated online fabric inspection by fast Fourier transform (FFT) and cross-correlation. Textile Research Journal, 83(3), 256–268. doi:10.1177/0040517512458340
  • Meng, S., Pan, R., Gao, W., Zhou, J., Wang, J., & He, W. (2019). Woven fabric density measurement by using multi-scale convolutional neural networks. IEEE Access, 7, 75810–75821. doi:10.1109/ACCESS.2019.2922502
  • Ouyang, W., Xu, B., Hou, J., & Yuan, X. (2019). Fabric defect detection using activation layer embedded convolutional neural network. IEEE Access, 7, 70130–70140. doi:10.1109/ACCESS.2019.2913620
  • 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.
  • Pan, R., Gao, W., & Liu, J. (2009). Color clustering analysis of yarn-dyed fabric in HSL color space. IEEE World Congress on Software Engineering, 2, 273–278. doi:10.1109/WCSE.2009.148
  • Pan, R., Gao, W., Liu, J., & 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), 81.
  • 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. doi:10.1007/s12221-011-0127-z
  • Pan, R., Liu, J., & Gao, W. (2013). Measuring linear density of threads in single-system-mélange color fabrics with FCM algorithm. Color Research & Application, 38(6), 456–462. doi:10.1002/col.21738
  • Perwass, C., & Wietzke, L. (2012). Single lens 3D-camera with extended depth-of-field. In Proceedings of SPIE Electronic Imaging on Human Vision and Electronic Imaging XVII (Vol. 8291, p. 829108). doi:10.1117/12.909882
  • Puente León, F. (1999). Automatische Identifikation von Schußwaffen. Fortschritt-Berichte VDI, Reihe 8 (Vol. 787). Düsseldorf: VDI Verlag.
  • Qiu, H. D., Lu, C. H., Chen, W., & Li, J. M. (2011). Investigation of laser current influence on two-dimensional bar code contrast. Advanced Materials Research, 314–316, 197–204. doi:10.4028/www.scientific.net/AMR.314-316.197
  • Tunák, M., Linka, A., & Volf, P. (2009). Automatic assessing and monitoring of weaving density. Fibers and Polymers, 10(6), 830–836. doi:10.1007/s12221-009-0830-1
  • Woodham, R. J. (1980). Photometric method for determining surface orientation from multiple images. Optical Engineering, 19(1), 139–144. doi:10.1117/12.7972479
  • Xiang, J., Zhang, N., Pan, R., & Gao, W. (2019). Fabric image retrieval system using hierarchical search based on deep convolutional neural network. IEEE Access, 7, 35405–35417. doi:10.1109/ACCESS.2019.2898906
  • Xiang, Z., Zhang, J., & Hu, X. (2018). Vision-based portable yarn density measure method and system for basic single color woven fabrics. The Journal of the Textile Institute, 109(12), 1543–1553. doi:10.1080/00405000.2018.1429244
  • Yildirim, P., Birant, D., & Alpyildiz, T. (2018). Data mining and machine learning in textile industry. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(1), e1228. doi:10.1002/widm.1228
  • Zhang, J., Pan, R., & Gao, W. (2015). Automatic inspection of density in yarn-dyed fabrics by utilizing fabric light transmittance and Fourier analysis. Applied Optics, 54(4), 966–972. doi:10.1364/AO.54.000966
  • Zhang, J., Pan, R., Gao, W., & Zhu, D. (2015). Automatic inspection of yarn-dyed fabric density by mathematical statistics of sub-images. The Journal of the Textile Institute, 106(8), 823–834. doi:10.1080/00405000.2014.946342
  • Zhang, R., & Xin, B. (2016). An investigation of density measurement method for yarn-dyed woven fabrics based on dual-side fusion technique. Measurement Science and Technology, 27(8), 85403. doi:10.1088/0957-0233/27/8/085403
  • Zhang, J., Xin, B., & Wu, X. (2014). Density measurement of yarn dyed woven fabrics based on dual-side scanning and the FFT technique. Measurement Science and Technology, 25(11), 115007. doi:10.1088/0957-0233/25/11/115007
  • Zhou, Z., Wang, C., Zhang, J., & Zhu, Z. (2019). Color difference classification of solid color printing and dyeing products based on optimization of the extreme learning machine of the improved whale optimization algorithm. Textile Research Journal, 90(2), 135–155. doi:10.1002/2015GL067448

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