Recognition of colored spun fabric interlacing point based on mixed color space and multiple kernel learning

被引：0
|
作者：
Gong X. [1 ]
Yuan L. [1 ,2 ]
Liu J. [3 ]
Yang Y. [1 ]
Liu M. [1 ]
Ke Z. [1 ]
Yan Y. [4 ]
机构：
[1] School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan, 430200, Hubei
[2] State Key Laboratory for Hubei New Textile Materials and Advanced Processing Technology, Wuhan Textile University, Wuhan, 430200, Hubei
[3] School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, 430200, Hubei
[4] Electronic Information School, Wuhan University, Wuhan, 430072, Hubei
来源：
Yuan, Li (yuanli@wtu.edu.cn) | 1600年 / China Textile Engineering Society卷 / 41期
关键词：
Colored spun fabric; Interlacing point recognition; Mixed color space; Multiple kernel learning; Support vector machine;
D O I：
10.13475/j.fzxb.20190804708
中图分类号：
学科分类号：
摘要：
Aiming at the difficulty in extracting feature parameters of colored fabric interlacing points, an automatic recognition algorithm for such interlacing points based on mixed color space and multiple kernel learning was established. Firstly, the channel having the same color properties among the three-color spaces of YUV, HSV and Lab was fused to construct a mixed color space. On this basis, the local texture features and the third-order color moment features of the image of colored fabric interlacing points were extracted to represent the interlacing points. Finally, support vector machine was constructed by multi-kernel learning algorithm to recognize interlacing point features. The experimental results indicate that the established recognition algorithm can not only effectively recognize the interlacing points in plain, twill and satin weave fabrics, but also has ideal robustness and universality for the adjustment of fabric components and yarn forming process. The average recognition rate achieved in this research reaches 91.2%. Copyright No content may be reproduced or abridged without authorization.
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页码：58 / 65
页数：7
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