The Performance Evaluation of Classic ICA Algorithms for Blind Separation of Fabric Defects
DOI:
10.3993/jfbi09201407
Journal of Fiber Bioengineering & Informatics, 7 (2014), pp. 377-386.
Published online: 2014-07
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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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