一种新的布匹瑕疵图像自动检测算法

doi:10.3969/j.issn.1001-2400.2011.05.011

Abstract

Abstract:

Considering the advantages that decomposition coefficients in the non subsampled Contourlet of fabric images can describe the contour characteristics in a better way, and that they have shift-invariant and multidirection, a novel algorithm for automated detection of fabric defect images is presented. Firstly, the nonsubsampled Contourlet transform (NSCT) is used to perform the sparse representations in multi-scales and multi-directions. On this basis, the optimal sub-bands of NSCT are selected by the cost function, and then the robust descriptions are obtained. Finally, the parameters of defect and nondefect images are timely estimated separately by the Mixture Gaussian Model(MGM), which effectively avoids estimating each defect and reduces the computational complexity evidently. Experimental results show that the proposed algorithm can lead to a better performance than the traditional algorithms in subjective effects and objective evaluation.

Key words: defect detection, nonsubsampled Contourlet transform(NSCT), mixture gaussian model(MGM), wavelet transforms

CLC Number:

TN911.73

CUI Lingling;LU Zhaoyang;LI Jing;LI Yihong. Novel algorithm for automated detection of fabric defect images[J].J4, 2011, 38(5): 65-72.

References

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Novel algorithm for automated detection of fabric defect images

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