基于线性规划和相似变换的点匹配算法

为解决点匹配过程中非刚性形变、位置噪声和出格点等因素导致点匹配不理想的问题，提出一种基于线性规划和相似变换的特征点匹配算法。点匹配被建模成一个能量函数最小化问题。在该函数中，形状上下文特征用于降低点对应关系的歧义性，相似变换用于保持空间映射的连续性，连续松弛问题归结为一个线性规划。仿真结果证实了该算法的有效性。

Point matching based on linear programming with similarity regularization

This paper proposed a linear programming based point matching method with similarity regularization in order to resolve the problems of non-rigid deformation, positional noise and outliers. Point matching was modeled as an energy minimization problem. Shape context was used to reduce the ambiguity of point correspondence, and similarity transform was used to preserve the continuity of spatial mapping. The continuously relaxed optimization problem is reduced to a linear program where optimality can be guaranteed. The simulation results verified the effectiveness of the method.