Automatic seam pucker evaluation using support vector machine classifiers
International Journal of Clothing Science and Technology
ISSN: 0955-6222
Article publication date: 28 November 2018
Issue publication date: 18 February 2019
Abstract
Purpose
The purpose of this paper is to propose a novel method using support vector machine (SVM) classifiers for objective seam pucker evaluation. Features are extracted using wavelet analysis and gray-level co-occurrence matrix (GLCM), and the samples are evaluated using SVM classifiers. The study aims to solve the problem of inappropriate parameters and large required samples in objective seam pucker evaluation.
Design/methodology/approach
Initially, seam pucker image was captured, and Edge detection and Hough transform were utilized to normalize the seam position and orientation. After cropping the image, the intensity was adjusted to the same identical level through histogram specification. Then, the standard deviations of the horizontal image and diagonal image, reconstructed using wavelet decomposition and reconstruction, were calculated based on parameter optimization. Meanwhile, GLCM was extracted from the restructured horizontal detail image, then the contrast and correlation of GLCM were calculated. Finally, these four features were imported to SVM classifiers based on genetic algorithm for evaluation.
Findings
The four extracted features reflected linear relationships among five grades. The experimental results showed that the classification accuracy was 96 percent, which catches up to the performance of human vision, and resolves ambiguity and subjective of the manual evaluation.
Originality/value
There are large required samples in current research. This paper provides a novel method using finite samples, and the parameters of the methods were discussed for parameter optimization. The evaluation results can provide references for analyzing the reason of wrinkles during garment manufacturing.
Keywords
Acknowledgements
The authors would like to acknowledge the Research Innovation Program for Postgraduate Research and Practice Innovation Program of Jiangsu Province (No. KYCX18_1819); the Fundamental Research Funds for the Central Universities (Nos JUSRP51631A and JUSRP11805); the National Natural Science Foundation of China (No. 61802152); the Natural Science Foundation of Jiangsu Province (No. BK20180602); and the Jiangsu Province Postdoctoral Science Foundation (No. 2018K037B).
Citation
Zhang, N., Pan, R., Wang, L., Wang, S., Xiang, J. and Gao, W. (2019), "Automatic seam pucker evaluation using support vector machine classifiers", International Journal of Clothing Science and Technology, Vol. 31 No. 1, pp. 2-15. https://doi.org/10.1108/IJCST-03-2018-0046
Publisher
:Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited