[1]朱安民,张艺,李观强.基于计算机视觉的纺织品色牢度检测研究[J].深圳大学学报理工版,2018,35(4):420-425.[doi:10.3724/SP.J.1249.2018.04420]
 ZHU Anmin,ZHANG Yi,and LI Guanqiang.An computer-vision-based system for textile color fastness testing[J].Journal of Shenzhen University Science and Engineering,2018,35(4):420-425.[doi:10.3724/SP.J.1249.2018.04420]
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基于计算机视觉的纺织品色牢度检测研究()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第35卷
期数:
2018年第4期
页码:
420-425
栏目:
电子与信息科学
出版日期:
2018-07-10

文章信息/Info

Title:
An computer-vision-based system for textile color fastness testing
作者:
朱安民张艺李观强
深圳大学计算机与软件学院,广东深圳 518060
Author(s):
ZHU Anmin ZHANG Yi and LI Guanqiang
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060,P.R.China
关键词:
计算机视觉图像处理色牢度检测色差等级评定ISO标准比色卡人工目测法纺织品质量控制
Keywords:
computer vision image processing color fastness testing evaluation on color difference ISO standard color card manual measurement quality control of texfile
分类号:
TP 391
DOI:
10.3724/SP.J.1249.2018.04420
文献标志码:
A
摘要:
利用计算机视觉和图像处理等技术设计并初步实现了计算机辅助检测纺织品色牢度系统.首先将采集到的样品图像由RGB转换到CIELAB色彩空间,通过调整转换参数、消除图像光照影响及噪音影响来完善系统.然后建立色差等级评定系统,并以ISO标准比色卡和实际布样为例进行验证. 检测结果表明,尽管采用计算机视觉与人工目测法的评级结果存在一定误差,但由测试数据的规律性变化可以推断该系统检测纺织品色牢度的方法基本正确,一定程度上能够用于实际检测,从而节省人力物力,提高测试效率.
Abstract:
Based on computer vision and image processing technology, a computer aided system for textile color fastness testing is designed and realized in this paper. By transforming the color space and adjusting the transformation parameters, the designed system eliminates the influence of image illumination and noise, so as to solve the problem of excessive error. Then the evaluation system for the level of the textile color fastness is established. This system is verified by the experiment testing with ISO standard color card and actual textile samples. Experiment results show that although there are some error existing, the method of the designed detection system for color fastness is correct. This system would be applied to reality in the future, thus saving a lot of manpower and money to improve test efficiency.

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更新日期/Last Update: 2018-06-20