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Objective assessment for characterising the flatness of garment sewing stitches

Objective assessment for characterising the flatness of garment sewing stitches Abstract In this paper, a novel classification method of assessing garment sewing stitch based on amended bi-dimensional empirical mode decomposition (ABEMD) has been introduced. Two parameters that characterise garment sewing stitch, average area and standard deviation, have been defined based on the grey value of pixels. Experimental results showed that when the window size is 512×128 pixels with regard to average area, the threshold can be decided as 6.00, 5.50, 5.30 and 4.00 for five different grades , respectively. Meanwhile, with regard to standard deviation, the threshold can be decided as 48.00, 40.00, 30.00 and 20.00, respectively. It is demonstrated that the parameters are effective in discriminating sewing stitch images in terms of the grades when used as inputs for the ABEMD. The performance of the algorithm on different garment status is significantly reliable. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Autex Research Journal de Gruyter

Objective assessment for characterising the flatness of garment sewing stitches

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Publisher
de Gruyter
Copyright
Copyright © 2013 by the
ISSN
1470-9589
DOI
10.2478/v10304-012-0037-1
Publisher site
See Article on Publisher Site

Abstract

Abstract In this paper, a novel classification method of assessing garment sewing stitch based on amended bi-dimensional empirical mode decomposition (ABEMD) has been introduced. Two parameters that characterise garment sewing stitch, average area and standard deviation, have been defined based on the grey value of pixels. Experimental results showed that when the window size is 512×128 pixels with regard to average area, the threshold can be decided as 6.00, 5.50, 5.30 and 4.00 for five different grades , respectively. Meanwhile, with regard to standard deviation, the threshold can be decided as 48.00, 40.00, 30.00 and 20.00, respectively. It is demonstrated that the parameters are effective in discriminating sewing stitch images in terms of the grades when used as inputs for the ABEMD. The performance of the algorithm on different garment status is significantly reliable.

Journal

Autex Research Journalde Gruyter

Published: Dec 31, 2013

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