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作 者:钟文彬[1] 李旭瑞 孙思 刘光帅[2] Zhong Wenbin;Li Xurui;Sun Si;Liu Guangshuai(The 10th Research Institute of China Electronics Technology Group Corporation,Chengdu,Sichuan 610036,China;School of Mechanical Engineering,Southwest Jiaotong University,Chengdu,Sichuan 610031,China)
机构地区:[1]中国电子科技集团公司第十研究所,四川成都610036 [2]西南交通大学机械工程学院,四川成都610031
出 处:《激光与光电子学进展》2020年第14期17-24,共8页Laser & Optoelectronics Progress
基 金:国家自然科学基金(51275431);中国电子科技集团公司第十研究所技术创新基金,十所计20181218。
摘 要:针对印制电路板关键轮廓特征提取难的问题,提出了一种将折边线转化为边界线,再进行关键轮廓线特征点提取的算法。该算法首先利用k维树对印制电路板原始点云数据建立拓扑结构,从而实现对k邻域点的快速查找,采用直通滤波算法完成对印制电路板点云的整体预处理;其次通过随机采样一致性算法将印制电路板中面积最大的平面特征单独提取出来,使关键轮廓特征实现了在空间上的分离;再采用基于法向量夹角限制条件的欧氏聚类完成折边特征的点聚类,从而实现将折边线转化为边界线的思想;最后根据k邻域点之间向量的夹角与设定阈值之间的大小关系,来判定查询点是否属于边界轮廓特征点。实验结果表明,该算法能够较为完整地提取出印制电路板点云的关键轮廓线特征信息。ing at the difficulty of extracting the key contour features of printed circuit boards,an algorithm for transforming the folded edge into the boundary and extracting the key contour feature points is proposed.First,the algorithm establishes a topological structure of the original point cloud data of the printed circuit board by using k dimensional-tree,and realize fast search of the closest k neighborhood points.Pass-through filtering algorithm is used to complete the pre-processing of the printed circuit board point cloud.Second,Random Sample Consensus algorithm is used to extract the plane features with the largest area in the printed circuit board separately,so that the key contour features are spatially separated.The point clustering of the fold edge feature is completed by Euclidean clustering based on normal angle and realize the idea of transforming the folded edge into the boundary.Finally,according to the relationship between set threshold and vector angle between k neighborhood points,one can determine whether the query point belongs to the boundary contour feature point.Experimental results show that the proposed algorithm can extract the key contour feature line of printed circuit board point cloud more completely.
关 键 词:图像处理 印制电路板 点云数据 特征轮廓线 随机采样一致性 聚类
分 类 号:TP391[自动化与计算机技术—计算机应用技术]
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