首页> 外文会议>2011 IEEE Recent Advances in Intelligent Computational Systems >Singular value decomposition method for the detection of defects in woven fabric refined by morphological operation
【24h】

Singular value decomposition method for the detection of defects in woven fabric refined by morphological operation

机译:用于形态学精制的机织物缺陷检测的奇异值分解方法

获取原文

摘要

In this paper a new approach for the detection of defects in woven fabric is presented where the singular value decomposition (SVD) method is used. SVD basically removes the interlaced grating structure of the waft and warp of the fabric leaving aside the defective part of the fabric. An intensity threshold value along with the module of definite size is considered for the binarization of the background free fabric image. Finally, for the removal of the noise from the binary fabric image the morphological opening operation with the suitable structuring element is performed. The technique is tested on 287 fabric samples consisting of five different types of defects in three types of woven fabrics from TILDA database. 94.08% success rate of detection of defects is achieved.
机译:本文提出了一种利用奇异值分解(SVD)方法检测机织物缺陷的新方法。 SVD基本上消除了织物的经线和经线的交错光栅结构,而将织物的有缺陷的部分放在一边。考虑将强度阈值与确定大小的模块一起用于无背景织物图像的二值化。最后,为了从二元织物图像中去除噪声,使用合适的结构元件执行形态学打开操作。该技术在TILDA数据库的三种类型的机织织物中的287种织物样品上进行了测试,这些样品包含5种不同类型的缺陷。缺陷检测成功率达到94.08%。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号