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| The Algorithm of ICA Based on PCA for Fabric Defect Detection |
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Abstract The Independent Component Analysis (ICA) algorithm based on
Principal Component Analysis (PCA) is described in this paper to
achieve the raw textile defect detection. In the first step, the
observed matrix $X$ is constructed from a large number of
defect-free sub-images and PCA is operated to achieve dimension
reduction. In the second step, the transformation matrix $W$ and
independent basis subspace $s$ are obtained from defect-free
sub-images through ICA. In the final step, feature extraction is
achieved from the overlapping sub-windows of a test image. Then a
sub-window is classified as defective or non-defective according to
Euclidean distance. The results have been analyzed in detail and
illustrated this approach has better performance in raw textile.
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| Cite this article: |
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Junfeng Jing,Juan Zhao,Pengfei Li, et al. The Algorithm of ICA Based on PCA for Fabric Defect Detection[J]. Journal of Fiber Bioengineering and Informatics, 2015, 8(4): 687-696.
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2015, Vol. 8 
