Abstract: Focusing on the problem that the component contours taken by smart machine tool visual system always contain abnormal regions caused by the background interference,a component contour modification method based on image registration was proposed. Firstly,the component template feature point set and the matched feature point set were extracted from the component engineering drawing and the real image. Secondly,the parameters in the affine transformation model were decomposed and analyzed,and a criterion function was established based on area characteristics and edge structure characteristics of feature point sets of both images. Thirdly,an improved genetic algorithm was used to search for affine transformation parameters corresponding to the global maximum similarity between two images. After the image registration, the abnormal contour segments were detected and replaced by calculating the optimal migrated piecewise Hausdorff distance between the template contour point set and the matched contour point set. Experimental results show that the proposed method can detect the abnormal contour segments in matching contour point set with high accuracy and stability,its registration accuracy is 50% higher than that of Square Summation Joint Feature(SSJF)method,and the distance where the modified contour intersects is less than 3 pixels.

Key words: contour modification, image registration, affine transformation model, genetic algorithm, Hausdorff distance

摘要： 针对智能机床视觉系统提取待加工零件边缘轮廓时易受到背景干扰，导致其提取出的零件轮廓中包含异常区域的问题，提出一种基于图像配准的高精度零件轮廓修正方法。首先，从零件工程图与真实图像当中提取出零件模板特征点集与待匹配特征点集；其次，对仿射变换模型中的参数进行分解分析，并利用两图特征点集中的面积特征与边缘结构特征构建准则函数；然后，使用改进的遗传算法搜索两图像全局最高相似度所对应的仿射变换参数，在图像配准之后，再通过计算最优迁移后的模板轮廓点集与待匹配轮廓点集的分段Hausdorff距离来检测并替换待匹配轮廓中的异常轮廓段。实验结果表明，该方法能精确、稳定地检测出待匹配轮廓点集中的异常轮廓段，配准精度比联合特征均方和（SSJF）方法高出50%，修正后轮廓交接点处的距离不超过3像素值。

关键词: 轮廓修正, 图像配准, 仿射变换模型, 遗传算法, Hausdorff距离