Characteristic Analysis of Textile Materials Based on Pre-Judgment Mechanism

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Abstract:

Aiming at existing some problems, such as large calculation, not extensive category of defect recognition, and not accuracy of recognition result, this paper reanalyzes characteristic construction’s proceed of moving image by human vision, and proposes a new ‘super-fuzzy’ characteristic model of fabric based on pre-judgment mechanism. In this model, ‘super-fuzzy’ factor is introduced, and pre-judgment mechanism on coarseness set is constructed. So the characteristic can be modified with optimization according to actual condition of image. From experiments, results show that this characteristic model has such features as calculation is less, fabric images analysis is fast, fabric and defect category is extensive, pre-learning of mechanism doesn’t need, and better application is expected.

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Key Engineering Materials (Volumes 460-461)

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562-565

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January 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] Zou Chao, Zhu Desen, Xiao Li: Textural defect detection based on label co-occurrence matrix. Journal of Huazhong University of Science and Technology. vol. 34-6(2006), p.25.

Google Scholar

[2] Feng Jian-hui, Yang Yu-jing: Study of Texture Images Extraction Based on Gray Level Co Occurence Matrix. vol. 3-1(2007), p.19.

Google Scholar

[3] J. Portilla E.P. Simoncelli: A Parametric Texture Model based on Joint Statistics of Complex Wavelet Coefficients. International Journal of Computer Vision, vol. 40-1(2002), p.49.

Google Scholar

[4] Soo Chang Kim, Tae Jin Kang: Texture classification and segmentation using incomplete tree structured wavelet packet frame and Gaussian mixture model. Imaging Systems and Techniques. vol. 13-5(2005), p.46.

DOI: 10.1109/ist.2005.1594525

Google Scholar

[5] Jianli Liu, Baoqi Zuo: Identification of fabric defects based on discrete wavelet transform and back-propagation neural network. Journal of the Textile Institute. vol. 98-4, p.355.

DOI: 10.1080/00405000701547193

Google Scholar

[6] A. Conci C.A. Proenca: A fractal image analysis system for fabric inspection based on box-counting method. Computer Networks and ISDN Systems. vol. 30-21(1998), p.1887.

DOI: 10.1016/s0169-7552(98)00211-6

Google Scholar

[7] H. Sari-sarraf,J. Goddard: Vision system for on-loom fabric inspection. IEEE Transactions on Industry Applications. vol. 35-6(1999), p.1252.

DOI: 10.1109/28.806035

Google Scholar

[8] F.S. Cohen,Z. Fan,S. Attali: Automated inspection of textile fabrics using textural models. IEEE Transactions on PAMI. vol. 13-8(1990), p.803.

DOI: 10.1109/34.85670

Google Scholar

[9] D.P. Brzakovi P.R. Bakic, et al: A generalized development environment for inspection of web materials. IEEE International conference on Robotics and Automation, vol. 12(1997), p.1.

Google Scholar

[10] Xu Zengbo, Gong Yunan and Huang Xiubao: Fabric Defects Detection with Wold-based Texture Model and Fractal Theory. Journal of China Textile University, vol. 26-1(2000), p.6.

Google Scholar

[11] Chetverikov Dmitry: Pattern regularity as a visual key. Image and Vision Computing, vol. 18-12(2000), p.975.

DOI: 10.1016/s0262-8856(00)00041-x

Google Scholar

[12] Chetverikov Dmitry, Hanbury A: Finding defects in texture using regularity and local orientation. Pattern Recognition, vol. 35-10(2002), p.2165.

DOI: 10.1016/s0031-3203(01)00188-1

Google Scholar