@Article{JFBI-7-377, author = {}, title = {The Performance Evaluation of Classic ICA Algorithms for Blind Separation of Fabric Defects}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2014}, volume = {7}, number = {3}, pages = {377--386}, abstract = {Independent Component Analysis (ICA) is a blind source separation technique that has been broadly used in signal and image separation. In order to verify the feasibility of ICA algorithms which will be used for the detection of fabric defect, four kinds of classic ICA algorithms have been chosen and compared in terms of their algorithm performances. The results of simulation experiments show that the separation performances of these algorithms are different and FastICA algorithm has the best separation performance than others.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbi09201407}, url = {http://global-sci.org/intro/article_detail/jfbi/4793.html} }