Abstract
We present an automatic method for the estimation of yarn locations in fabric images. The proposed method is based on histogram segmentation and a so-called monotone hypothesis which is a nonparametric statistical approach. In this method, accumulated partial derivatives histograms are statistically unimodal, which indicate the periodic structures of fabric images. Then, the monotone hypothesis is applied to divide the histograms into several segments. According to the maximum value and the minimum value of the histograms in each segment, the locations of yarn boundaries and yarn centers can be correspondingly estimated. The method reduces the influence of yarn random texture noise that comes from yarn hairiness, improving the accuracy of detection. Furthermore, compared with classical method based on image smoothing, the proposed method can avoid over-smoothing of the edges of yarns.
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References
Pan, R., Gao, W., Li, Z., Gou, J., Zhang, J., Zhu, D.: Measuring thread densities of woven fabric using the Fourier transform. Fibres Text. East. Eur. 23(1), 35–40 (2015)
Pan, R., Zhang, J., Li, Z., Gao, W., Xu, B., Li, W.: Applying image analysis for automatic density measurement of high-tightness woven fabrics. Fibres Text. East. Eur. 24(2), 66–72 (2016)
Zhang, J., Pan, R., Gao, W.: Automatic inspection of density in yarn-dyed fabrics by utilizing fabric light transmittance and Fourier analysis. Appl. Opt. 54(4), 966 (2015)
He, F., Li, L., Xu, J.: Woven fabric density measure based on adaptive wavelets transform. J. Text. Res 28(2), 32–35 (2007)
Jing, J., Liu, S., Li, P., Li, Q., Liu, S., Jiang, M.: Automatic density detection of woven fabrics via wavelet transform. J. Inf. Comput. Sci. 11(8), 2559–2568 (2014)
Technikova, L., Tunak, M.: Weaving density evaluation with the aid of image analysis. Fibres Text. East. Eur. 98(2), 74–79 (2013)
Pan, R., Gao, W., Liu, J., Wang, H.: Automatic inspection of woven fabric density of solid colour fabric density by the Hough transform. Fibres Text. East. Eur. 81(4), 46–51 (2010)
Zhang, J., Gao, W., Pan, R.: A backlighting method for accurate inspection of woven fabric density. Ind. Textila 68(1), 31–36 (2017)
Pan, R., Liu, J., Gao, W.: Measuring linear density of threads in single system melange color fabrics with FCM algorithm. Color Res. Appl. 38(6), 456–462 (2013)
Zhang, J., Pan, R., Gao, W., Zhu, D.: Automatic inspection of yarn-dyed fabric density by mathematical statistics of sub-images. J. Text. Inst. 106(8), 823–834 (2015)
Zheng, D., Wang, L.: Multi-scale density detection for yarn-dyed fabrics with deformed repeat patterns. Text. Res. J. 87(20), 2524–2540 (2017)
Aldemir, E., Özdemir, H., Sarı, Z.: An improved gray line profile method to inspect the warp–weft density of fabrics. J. Text. Inst. 110(1), 105–116 (2019)
Delon, J., Desolneux, A., Lisani, J.L., Petro, A.B.: Automatic color palette. In IEEE International Conference on Image Processing 2005, vol. 2, pp. II–706. IEEE (2005)
Delon, J., Desolneux, A., Lisani, J.L., Petro, A.B.: A nonparametric approach for histogram segmentation. IEEE Trans. Image Process. 16(1), 253–261 (2007)
Delon, J., Desolneux, A., Lisani, J.L., Petro, A.B.: Automatic color palette. Inverse Probl. Imaging 1(2), 265–287 (2007)
Lu, C., Xu, L., Jia, J.: Real-time contrast preserving decolorization. In: SIGGRAPH Asia 2012 Posters, p. 161. ACM, Singapore (2012)
Eduardo, B.C., Jan-Olof, E.: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, p. 967. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10268-4
Le Hegarat-Mascle, S., Aldea, E., Vandoni, J.: Efficient evaluation of the number of false alarm criterion. EURASIP J. Image Video Process. 2019(1), 35 (2019)
Rajaei, B., von Gioi, R.G., Morel, J.: From line segments to more organized gestalts. In: 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp. 137–140. IEEE (2016)
Miriam, A., Brunk, H.D., Ewing, G.M., Reid, W.T., Edward, S.S.: An empirical distribution function for sampling with incomplete information. Ann. Math. Stat. 26(4), 641–647 (1955)
Desolneux, A., Moisah, L., Morel, J.M.: A grouping principle and four applications. IEEE Trans. Pattern Anal. Mach. Intell. 25(4), 508–513 (2003)
Pan, R., Gao, W., Liu, J., Wang, H., Zhang, X.: Automatic detection of structure parameters of yarn-dyed fabric. Text. Res. J. 80(17), 1819–1832 (2010)
Zheng, D.: A new method for automatic separation of fabric color. Text. Res. J. 85(14), 1520–1538 (2015)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (Grants 61872429, 61402290, 61472257 and 61772343); in part by the Natural Science Foundation of Guangdong (Grant 1714050003822); and in part by the Natural Science Foundation of Shenzhen (Grants JCYJ20170818091621856).
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Han, Y., Luo, L. (2019). Automatic Inspection of Yarn Locations by Utilizing Histogram Segmentation and Monotone Hypothesis. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_14
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