Pre-Judgment Model of Image Based on Gray Distribution Wave

Abstract:

Aiming to the problem of low computing speed of existing model of fabric characteristic, this paper introduces pre-judgment to improve the speed of image analysis by fabric characteristic model, and constructs gray distribution wave model to quickly estimate image. First, image gray is taken. Then, horizontal and vertical gray distribution wave is established using superposition formula with little computation to further simply model and made the computation speed fast. Finally, from a number of experiments, this model has such features that image can be pre-estimated exactly and continue recognition algorithm can be instructed to leap over some normal regions. So analysis times of image are saved and on-line recognition efficiency of fabric defects is improved.

• Periodical: Applied Mechanics and Materials (Volumes 26-28)
• Pages: 155-158
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.26-28.155
• Online since: June 2010
• Authors: Xiu Chen Wang, Zhe Liu
• Keywords: Gray Distribution Wave, Image, Model, Pre-Judgment

References

[1] D.R. Rohrmus. Invariant and adaptive geometrical texture features for defect detection and classification. Pattern Recognition. vol. 38-2(2003), p.1546.

DOI: 10.1016/j.patcog.2005.02.004

[2] F. Tajeripour, E. Kabir and A. Sheikhi. Defect Detection in Patterned Fabrics Using Modified Local Binary Patterns. International Conference on Computational Intelligence and Multimedia Applications. vol. 2(2007), p.63.

DOI: 10.1109/iccima.2007.50

[3] A. Kumar. Neural network based detection of local textile defects. Pattern Recognition. vol. 36(2003), p.1645.

DOI: 10.1016/s0031-3203(03)00005-0

[4] G.H. S Arunkumar, F. Eric. Statistical approach to unsupervised defect detection and multiscale localization in two-texture images. Optical Engineering. vol. 47-2(2008), p.20.

DOI: 10.1117/1.2868783

[5] Chung-feng Jeffrey KUO, Chung-yang, SHIH, et al. Automatic Recognition of Fabric Weave Patterns by a Fuzzy C-Means Clustering Method. Textile Research Journal. vol. 74-2(2004), p.107.

DOI: 10.1177/004051750407400204

[6] Du-Ming Tsai, Chih-Chia Kuo. Defect detection in inhomogeneously textured sputtered surfaces using 3D Fourier image reconstruction. Machine Vision and Applications, vol. 18-6(2007), p.383.

DOI: 10.1007/s00138-007-0073-3

[7] K.L. Mak and P. Peng. An automated inspection system for textile fabrics based on Gabor filters. Robotics and Computer-Integrated Manufacturing. Vol. 24-6(2008), p.359.

DOI: 10.1016/j.rcim.2007.02.019

[8] Chang-Chiun Huang and Tong-Fu Lin. Image Inspection of Nonwoven Defects Using Wavelet Transforms and Neural Networks. Fibers and Polymers. vol. 9-5(2008), p.633.

DOI: 10.1007/s12221-008-0099-9

[9] HE Feng LI Liqing XU Jianming. Woven fabric density measure based on adaptive wavelets transform. Journal OF Textile Research. vol. 28-2(2007), p.32.

[10] HOU Biao LIU Feng JIAO Li-cheng BAO Hui-dong. A Multiscale Texture Image Segmentation Algorithm Based on Adaptive Window Fixing and Propagation. Acta Electronica Sinica. vol. 37-7(2009), p.1492.

[11] HAN Li-wei XU De WANG Lin-kun. Adaptive Detection of Fabric Flaw Based on Statistical Information. Chinese Journal of Electron Devices. vol. 31-3(2008), p.979.