A study of automated color,shape and texture analysis of Tatami embroidery fabrics

Abstract

This paper proposes an automated analysis system for Tatami embroidery fabric images that automates color analysis, pattern shape analysis and texture analysis. Firstly, the RGB (red, green and blue) image of embroidery fabric is obtained by a color scanner. In color analysis, the wavelet transforms and median filter are used for RGB image preprocessing, and then the Fuzzy C-Means (FCM) clustering method is used for the binary region splitting method, the color features of statistical values of colors and number of colors can be obtained. In the pattern shape analysis, the individual pattern components are segmented by the separated colors, and the shape features of pattern components are obtained by moment invariants. Finally, in the texture analysis of Tatami embroidery fabrics, the gray image of the weave block is segmented from the pattern component region, after the entropy filter, the parallel division lines are segmented by a threshold value, and then the angle and spacing between Tatami embroidery parallel division lines are obtained by Hough transform to represent texture features. The experimental results show that this method can implement Tatami embroidery fabric color, shape and texture analyses automatically.

Keywords

Tatami embroidery fabrics color analyses shape analyses texture analyses

Get full access to this article

View all access options for this article.

References

Marios H and Kostas E. Computer graphical representation of design data in embroidery factories. In: proceedings of the 7th WSEAS international conference on automation & information, Cavtat, Croatia, 2006, pp.66–70.

Shan

HYJSC

Yong

. An algorithm of finding path of embroider suture needle. Chin J Comput 2003; 9: 025–025.

Yang

Zhang

. An embroidery simulation algorithm based on GDI. J Eng Graph 2007; 5: 008–008.

Chen X, McCool M, Kitamoto A, et al. Embroidery modeling and rendering. In: proceedings of graphics interface, Canadian Information Processing Society, 2012, pp.131–139.

Yang K, Zhou J, Sun Z, et al. Image-based irregular needling embroidery rendering. In: proceedings of the 5th international symposium on visual information communication and interaction, 2012, pp.87--94.

Fan

. Simulation of pattern on computerized embroidery based on image processing. Appl Mech Mater 2013, pp. 1675–1679.

Kuo

CFJ

Jian

. Automatic machine embroidery image color analysis system. Part I: Using Gustafson-Kessel clustering algorithm in embroidery fabric color separation. Text Res J 2012; 82: 571–583.

Kuo

CFJ

Kao

Jian

. Automatic machine embroidery image color analysis system, part III: integration of machine embroidery image color analysis system. Text Res J 2012; 82: 2090–2098.

Yang

Huang

Liu

. An edge-oriented clustering technology for preserving the critical features in embroidery images. Adv Inform Sci Serv Sci 2015; 7: 150–167.

10.

Dongcheng

TQLYH

. Shape decomposition algorithm in embroidery. J Comput Aid Des Comput Graph 2005; 12: 006–006.

11.

Hu SL and Fan XJ. The research on graphic outline capturing algorithm based on coloured embroidery pattern of HIS space. In: IEEE 2011 10th international conference on electronic measurement & instruments, Vol. 3, 2011, pp.320–323.

12.

Fan

. Research on edge detection algorithm of color embroidery patterns based on HSI color space. J Beijing Inst Cloth Technol 2012; 32: 65–69.

13.

Kuo

CFJ

Hsu

CTM

Shih

. Automatic pattern recognition and color separation of embroidery fabrics. Text Res J 2011; 81: 1145–1157.

14.

Kuo

CFJ

Jian

Tung

. Automatic machine embroidery image color analysis system, part II: application of the genetic algorithm in search of a repetitive pattern image. Text Res J 2012; 82: 1099–1106.

15.

Kuo

CFJ

Shih

Hsu

CTM

. Pattern-making simulation on embroidery using probabilistic neural network and texture fitting method. Text Res J 2011; 81: 2082–2094.

16.

Liu

Guo

Liu

. The computer intelligent recognition of embroidery pictures. Comput Knowl Technol 2012; 35: 055–055.

17.

Watson

. Wavelet transforms on vector spaces as a method of multi-spectral image characterisation. IEE Proc Vis Image Signal Process 1997; 144: 89–97.

18.

Lim

Lee

. On the color image segmentation algorithm based on the thresholding and the Fuzzy C-means techniques. Pattern Recognit 1990; 23: 1235–1252.

19.

. Visual pattern recognition by moment invariants. Information Theory, IRE Transactions on 1962; 8: 179–187.

20.

Umbaugh

. Computer vision and image processing, PTR, NJ: Prentice Hall, 1999.