TY - JOUR
T1 - The Algorithm of ICA Based on PCA for Fabric Defect Detection
AU - Junfeng Jing, Juan Zhao, Pengfei Li, Hongwei Zhang & Lei Zhang
JO - Journal of Fiber Bioengineering and Informatics
VL - 4
SP - 687
EP - 696
PY - 2015
DA - 2015/08
SN - 8
DO - http://doi.org/10.3993/jfbim00166
UR - https://global-sci.org/intro/article_detail/jfbi/4750.html
KW - Textile Defect Detection
KW - Feature Extraction
KW - Principal Component Analysis
KW - Independent Component Analysis Heading
KW - Introduction
KW - Times New Roman
KW - Number
AB - 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.