Detection results can be affected by foreign matters such as dust and scurf since they lead to the contour of workpieces changed in vision detection system, even mini-parts have cleaned yet. Therefore, a study of location and elimination of foreign matters algorithm is presented based on priori knowledge of target contour extracted by region growing. The algorithm, no matter their positions and shapes, especially suits for foreign matters which have an obviously gray level compared to objects. Firstly, a work-piece image with foreign matters was obtained. A segmentation algorithm based on region was employed for priori knowledge on contour. Then, in order to eliminate an outer contour of a foreign matter, the corner of this contour were located from the curvature point of view. Finally, the disconnected outer contour was intelligently repaired according to the built priori knowledge. Experimental results indicate that the measurement accuracy does not reduce and the error of measuring result is within 6 μm. No matter if there are foreign matters on the surface of parts, this method can still obtain the right judgments. Additionally, it also can guarantee the accuracy of measurement unchanged, so as to improve the reliability of the whole vision detecting system.%工业现场清洁过的微型工件表面仍会有少量灰尘、发屑等附着物存在,在微型工件的视觉检测系统中会因其改变提取的目标轮廓而影响检测结果.为此,以灰尘与工件存在较小差异的任意位置和形状的一类附着物为考察对象,以区域生长提取的目标轮廓为先验背景,研究附着物定位与剔除算法.首先,荻取沾染附着物的工件图像,采用基于区域的分割算法做处理,以建立工件轮廓的先验知识;其次,从曲率角度定位附着物轮廓角点,以此剔除附着物轮廓;最后,根据先验知识自动修复断开的外轮廓.实验结果显示,加入附着物去除与修复算法后测量精度没有降低,测量结果误差6 μm以内,图像边缘的定位准确度能够给予保证.表明所研究的附着物定位与剔除方法使检测系统在允许微小附着物存在并且不影响测量精度的情况下,实现了目标轮廓的正确判别,提高了视觉检测系统的可靠性.
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