磁瓦表面图像的自适应形态学滤波缺陷提取方法

结合磁瓦表面机器视觉自动识别系统的需求,提出一种基于自适应形态学滤波的缺陷提取方法.针对磁瓦表面缺陷对比度低、图像中存在磨痕纹理背景和整体的亮度不均匀等难点,设计了一种新的自适应形态学滤波器;根据磁瓦表面图像不同区域内的灰度变化进行分区域逐行扫描,估算每行缺陷最大尺寸,使滤波器的一维棒状结构元素随着缺陷尺寸自动调整;通过逐行自适应形态学滤波滤除或弱化缺陷,模拟出背景图像(阈值曲线),用原始图像与背景图像相比较即可提取出磁瓦表面的缺陷.实验结果表明,该方法能准确、快速地提取出磁瓦表面图像各区域的缺陷,通用性好,可用于磁瓦缺陷在线自动识别系统.

Defect Extraction Method of Arc Magnet Surface Images Based on Adaptive Morphological Filtering

Based on adaptive morphological filtering,a defect extraction method is proposed in this paper to meet needs of machine vision automatic identification system of the arc magnet’s surface.To extract defects with low contrast,textured background and uneven brightness,the method presents a novel adaptive morphological filter.Sub-regional and progressive scan was executed to estimate maximum size of defects on each line according to gray level variety in different regions of the image.The one-dimensional rod-like structural elements of each morphological filter could be adjusted automatically with maximum size of defects.The defects are removed or weakened by progressive and adaptive morphological filtering,and the image background(threshold curve) was obtained.Then defects in the inspected surface could be extracted by comparing the original image with the background image.The experimental results indicate that the proposed method can extract defects of different sub-regions rapidly and accurately with good versatility,and can be used for online automatic identification to defects.