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

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.