Computer Engineering and Applications ›› 2017, Vol. 53 ›› Issue (17): 186-191.DOI: 10.3778/j.issn.1002-8331.1603-0355

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Paper defect detection based on CB morphology and gray-code decomposition

KANG Jie, PAN Silu, WANG Xiaodong   

  1. School of Electrical and Information Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
  • Online:2017-09-01 Published:2017-09-12


亢  洁,潘思璐,王晓东   

  1. 陕西科技大学 电气与信息工程学院,西安 710021

Abstract: Traditional paper defect detection algorithms have the problem of poor anti-interference ability, inaccurate positioning, complex computation. Considering this, a paper defect detection algorithm based on CB morphology and gray-code decomposition is presented. Firstly, the noise of the images containing paper defects is filtered by multi-scale CB morphology. Then, the filtered images are decomposed by gray-code decomposition. Finally, the edge of the important bitplane is detected by multi-structural elements CB morphology. The simulation results show that, this method is easily calculated, has a better anti-interference ability, and can accurately locate the paper defects.

Key words: Contour Bougie(CB) morphology, gray-code decomposition, paper defect detection

摘要: 传统纸病检测算法抗干扰能力差、定位不准确和运算复杂,针对该问题,提出了一种基于轮廓结构元素形态学和灰度码分解的纸病检测算法。首先,采用多尺度CB形态滤波算法对纸病图像进行滤波,再进行灰度码分解,最后运用多结构元素CB形态学提取重要位面图的边缘。仿真结果表明,该算法运算简单,具有较好的抗干扰能力,并能够较准确地定位纸病缺陷。

关键词: CB形态学, 灰度码分解, 纸病检测