纺织学报 ›› 2012, Vol. 33 ›› Issue (4): 12-18.

• 纤维材料 • 上一篇    下一篇

基于纤维纵向显微图像的棉/亚麻单纤维识别

应乐斌1,戴连奎1,吴俭俭2,孙国君2   

  1. 1. 浙江大学工业控制技术国家重点实验室
    2. 浙江省出入境检验检疫局
  • 收稿日期:2011-05-24 修回日期:2012-01-13 出版日期:2012-04-21 发布日期:2012-03-23
  • 通讯作者: 戴连奎 E-mail:lkdai@iipc.zju.edu.cn
  • 基金资助:

    市级

Identification of Single Cotton Fiber and Flax Fiber Based on Microscopic Fiber Images in the Longitudinal View

 YING  Le-Bin, DAI  Lian-Kui, WU  Jian-Jian, SUN  Guo-Jun   

  • Received:2011-05-24 Revised:2012-01-13 Online:2012-04-21 Published:2012-03-23

摘要: 针对棉/亚麻混纺纤维构成的织物,基于其单纤维纵向显微图像(纤维切段的长度约为0.5mm),研究了纤维的自动识别方法。在纤维检测中,先对纤维图像进行去背景处理,而后运用形态学闭运算和背景区域生长相结合的方法获得纤维的目标区域,对图片中出现的玻璃划痕、干扰杂物等进行了很好的滤除。由纤维骨架垂直方向上的区域图、二值图和细化图得到它们的垂直积分投影序列,并提取这3条序列各自的变异系数CV值和平均值共计6个参数。将这6个参数作为棉/亚麻纤维的特征参数,训练最小二乘支持向量机分类器,对测试集的测试结果表明该分类器对棉/亚麻短纤维的识别率正确率平均为93.3%。

Abstract: According to cotton-flax blended fabric, a new automatic identification method of microscopic fiber images in the longitudinal view in which the length of fiber is about 0.5 mm is introduced in this paper. In fiber detection section, the background of fiber image is removed first, then fiber areas are detected by a method combining morphological close computing and background regional growth method, which filters glass scratches and other sundries well. Based on the region image, binary image and thinning image of binary image in fiber skeleton vertical direction and their vertical integral projections, the coefficient variation (CV) and mean for each of the projections are obtained. Such six statistics parameters are used as the characteristic parameters for identification of cotton fibers and flax fibers. The training set is used to train the least square support vector machine classifier. The experiments for the testing set showed that the mean identification accuracy of cotton and flax fiber is 93.3%.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!