Cotton image segmentation method for online foreign fiber inspection

被引：0
|
作者：
Kan D. [1 ]
Li D. [1 ]
Yang W. [1 ]
Zhang X. [1 ]
机构：
[1] College of Information and Electrical Engineering, China Agricultural University
来源：
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | 2010年 / 26卷 / SUPPL. 2期
关键词：
Cotton; Foreign fiber; Image processing; Image segmentation;
D O I：
10.3969/j.issn.1002-6819.2010.z2.003
中图分类号：
S3 [农学（农艺学）]; S5 [农作物];
学科分类号：
0901 ;
摘要：
Image segmentation is a key technology for foreign fiber inspection in cotton based on machine vision. The image of cotton containing foreign fiber has a feature of that the background (cotton fiber) is homogeneous and has a normal gray-level distribution; the object (foreign fiber) is smaller, darker than the background but its gray-level distributes in a wide range. In this paper, a Background Estimation Thresholding(BET) method was presented to segment the objects from such kind of cotton images. Three typical kinds of cotton images were selected for the use of experiments and compared with Otsu method. BET method obtained better segmentation results than the Otsu's and was implemented fast, which consumed only 8.46s for 1 million times of segmentation. The experimental results show that the BET is effective and fast, and can be used in the online foreign fiber inspection in volumes of cotton.
引用
收藏
页码：11 / 15
页数：4
相关论文
共 9 条
  • [1] Yang W., Li D., Zhu L., Et al., A new approach for image processing in foreign fiber detection, Computers and Electronics in Agriculture, 68, 1, pp. 68-77, (2009)
  • [2] Lieberman M.A., Bragg C.K., Brennan S.N., Et al., Determining gravimetric bark content in cotton with machine vision, Textile Research Journal, 68, 2, pp. 94-104, (1998)
  • [3] Otsu N., A threshold selection method from gray-level histograms, IEEE Trans. On Systems, Man, and Cybernetics, SMC-9, 1, pp. 62-66, (1979)
  • [4] Hou Z., Hu Q., Nowinski, On minimum variance thresholding, Pattern Recognition Letters, 27, pp. 1732-1743, (2006)
  • [5] Ng H.-F., Automatic thresholding for defect detection, Pattern Recognition Letters, 27, pp. 1644-1649, (2006)
  • [6] Kapur J.N., Sahoo P.K., Wong A.K.C., Et al., A new method for gray-level picture thresholding using the entropy of histogram, Computer Vision, Graphics, and Image Processing, 29, pp. 273-285, (1985)
  • [7] Pun T., A new method for grey-level picture thresholding using the entropy of the histogram, Signal Processing, 2, pp. 223-237, (1980)
  • [8] Huang L., Wang M.J.J., Image thrsholding by minimizing the measures of fuzziness, Pattern Recognition, 28, 1, pp. 41-51, (1995)
  • [9] Kittler J., Illingworth J., Minium error thresholding, Pattern Recognition, 19, 1, pp. 41-47, (1986)
1