耳廓点云形状特征匹配的路径跟随算法

路径跟随算法结合凸松弛方法与凹松弛方法,通过跟随凸凹问题的解路径,近似地求解图匹配问题,具有较高的匹配精度.将路径跟随算法用于耳廓特征图的匹配问题:首先,基于PCA方法构造耳廓点云的显著性关键点集合;然后,采用乘积型参数域上的单值二次曲面方法拟合关键点邻域内的点集,并将曲面的局部形状特征定义为耳廓的局部形状相似测度;第三,对关键点集合进行Delaunay三角剖分,得到关键点集合在三维空间内的拓扑结构图,并定义关键点图的整体结构差异测度;最后,记耳廓关键点图的组合差异测度为关键点图的整体结构差异测度与关键点上的局部形状相似测度的线性组合,并基于路径跟随算法快速求解关键点图之间的精确匹配.相关实验结果表明:与其他相关算法相比,该算法具有较高的匹配效率和匹配精度.

Shape Feature Matching Algorithm of Ear Point Cloud Using Path Following

Combining the convex and relaxations, and following the solution path of convex-concave problem, the path following algorithm exhibits an excellent accuracy on graph matching approximately. In this paper, the path following algorithm is employed to address the problem of ear matching. Firstly, the PCA method is used to construct the set of salient keypoints of 3D ear point cloud data. Then the neighborhood of each keypoint is fitted to a single-valued quadric surface on a tensor-product parameter domain to define the local shape feature on the surface as the similarity measures. Next, the keypoints are triangulated into 3D topological graph using Delaunay triangulation, and the global structure discrepancy on the graph is obtained. Finally, the overall similarity measure is marked as the linear interpolation combination of the graph structure discrepancy and the local shape feature discrepancy, and the path following algorithm is then used to address the optimal matching between two keypoint graphs. The experiments show that the presented method provides a better matching result in terms of efficiency and accuracy than other similar approaches.