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 nondefective
according to Euclidean distance. The results have been analyzed in detail and illustrated this
approach has better performance in raw textile.