基于CB形态学和灰度码分解的纸病检测  

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

作　　者：亢洁[1] 潘思璐 王晓东[1] KANG Jie;PAN Silu;WANG Xiaodong(School of Electrical and Information Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China)

机构地区：[1]陕西科技大学电气与信息工程学院,西安710021

出　　处：《计算机工程与应用》2017年第17期186-191,共6页Computer Engineering and Applications

基　　金：陕西省自然科学基础研究计划项目(No.2014JM8329);陕西省教育厅专项科研计划项目(No.14JK1092);咸阳市科技计划项目(No.2011K07-03);陕西科技大学博士科研启动基金(No.BJ10-10)

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。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 graycode 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.

关 键 词：CB形态学 灰度码分解 纸病检测 

分 类 号：TS736.2[轻工技术与工程—制浆造纸工程]