Abstract

This study proposes a novel technique for detecting defects in fabric image based on the features extracted using a new multi resolution analysis tool called Digital Curvelet Transform. The direction features of curvelet coefficients and texture features based on GLCM of curvelet coefficients act as the feature-sets for a k-nearest neighbor classifier. The validation tests on the developed algorithms were performed with images from TILDA’s Textile Texture Database. A comparative study between the GLCM-based, wavelet-based and the curvelet-based techniques has also been included. The high accuracy achieved by the proposed method suggests an efficient solution for fabric defect. Furthermore, the algorithm has good robustness to white noise. Note that, this study is the first documented attempt to explore the possibilities of a new multi resolution analysis tool called digital Curvelet Transform to address the problem of fabric defect.

Keywords:

Coefficient correlation, curvelets, fabric defect detection, image denoising, wavelets,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.