Volume 7, Issue 3
The Performance Evaluation of Classic ICA Algorithms for Blind Separation of Fabric Defects

Hailan Zhang & Zhonghao Cheng

Journal of Fiber Bioengineering & Informatics, 7 (2014), pp. 377-386.

Published online: 2014-07

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  • 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.

  • Keywords

ICA Algorithm Signal and Image Separation Performance Evaluation Fabric Defect

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COPYRIGHT: © Global Science Press

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@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} }
TY - JOUR T1 - The Performance Evaluation of Classic ICA Algorithms for Blind Separation of Fabric Defects JO - Journal of Fiber Bioengineering and Informatics VL - 3 SP - 377 EP - 386 PY - 2014 DA - 2014/07 SN - 7 DO - http://doi.org/10.3993/jfbi09201407 UR - https://global-sci.org/intro/article_detail/jfbi/4793.html KW - ICA Algorithm KW - Signal and Image Separation KW - Performance Evaluation KW - Fabric Defect AB - 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.
Hailan Zhang & Zhonghao Cheng. (2019). The Performance Evaluation of Classic ICA Algorithms for Blind Separation of Fabric Defects. Journal of Fiber Bioengineering and Informatics. 7 (3). 377-386. doi:10.3993/jfbi09201407
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