摘要
针对模板匹配方法用于非结构化环境下的杂乱零件识别与定位精度低的问题,提出了亚像素级的配准算法.首先提出亚像素级的配准问题可视为计算模板图像与待配准图像的边缘点集的最优几何变换;然后提出了改进迭代最近点法来估计几何变换,包括使用动态邻域搜索策略快速搜索匹配点、利用匹配点的方向一致性约束和距离约束来移除误匹配点、用光照不变的点到曲线的距离来构造误差度量函数,再用线性最小二乘法给出误差函数的封闭解;最后使用自制零件和MPEG-7 shape B数据集进行实验,结果表明该算法配准正确率、实时性和精度均明显优于传统算法,能够满足非结构化环境下的杂乱零件亚像素配准精度和鲁棒性要求.
In the non-structural industrial environment, accurate registration method against inaccuracy of clutter parts recognition and location by template matching method was investigated. First of all, it was proposed that the sub-pixel accuracy of pattern recognition was equivalent to solve image geometry transformation. Then, improved iterative closest point was illustrated, including using the dynamic neighborhood search strategy to locate matching points quickly, removing the false matching points based on the distribution law of matching points, using point to curve distance metric to construct the error metric function, obtaining the closed-form solution of error metric function. At last, simulation image and real image were tested accordingly. The results showed that the registration accuracy, positioning accuracy and real-time of the proposed algorithm were better than traditional method significantly. It met the requirements of subpixel registration accuracy and robustness in the unstructured environment.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2016年第8期1242-1249,共8页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(31571568
51578162)
广州市科技计划项目(201510010140)
关键词
非结构化环境
迭代最近点法
图像配准
亚像素
non-structural environment
iterative closest point
image registration
sub-pixel
