结合先验信息的渐近式角点定位

A Method of Symptotic Corner Detection Based on Prior Information

针对批量微小型二维工件在视觉检测中角点的定位,提出一种渐近式分步提取方法。该方法以图像单像素轮廓上固有的几何关系为先验信息,首先在轮廓序列中锁定本次待测角点的存在区域,然后根据角点特征灵活设置响应函数检测角点,并对其精确定位及时排除伪角点,之后重新锁定区域。这一过程循环渐近,直至将所有角点检测出。实验结果表明,对同一工件不同位姿多次采像检测,角点间距的标准差在0.5 pixel以内,重复性高。与Harris角点检测、CSS角点检测算法比较,对视场内杂质背景的抗干扰性强,定位准确、快速。该角点定位方法在微小型二维工件的大批量高速检测中,具有明显优势。

A step-by-step asymptotic method of image corner detection is proposed in high speed vision detection for quantities of micro two-dimensional workpieces. In this method, the inherent geometric relationship in the single pixel contour is taken as a priori information, based on which, the region where corners exist is separated from full contour. After that, we select suitable algorithm to detect corners and get their positions accurately along with false corners being eliminated in time. Then, we can lock region again... This process continues circularly, until all the corners have been detected. Experimental results show that, for the same workpiece, whose images are getting in different position and attitude, the standard deviation of distance between points is within 0.5 pixels. Compared with Harris corner detection algorithm and CSS corner detection algorithm, the proposed method is scarcely affected by foreign substance in the field. Due to the accuracy, high speed and high repeatability, this method has obvious advantage in high speed vision detection for quantities of micro two-dimensional workpieces.