POINT PATTERN MATCHING ALGORITHM BASED ON POINT PAIR TOPOLOGICAL CHARACTERISTICS AND SPECTRAL MATCHING

Most of the Point Pattern Matching (PPM) algorithm performs poorly when the noise of the point's position and outliers exist. This paper presents a novel and robust PPM algorithm which combined Point Pair Topological Characteristics (PPTC) and Spectral Matching (SM) together to solve the afore mentioned issues. In which PPTC, a new shape descriptor, is firstly proposed. A new comparability measurement based on PPTC is defined as the matching probability. Finally, the correct matching results are achieved by the spectral matching method. The synthetic data experiments show its robustness by comparing with the other state-of-art algorithms and the real world data experiments show its effectiveness.

基于拟Laplacian谱和点对拓扑特征的点模式匹配算法

针对谱匹配方法对噪声和出格点的鲁棒性较差的问题,提出了一种基于拟Laplacian谱和点对拓扑特征的点模式匹配算法。首先,用赋权图的最小生成树构造无符号Laplacian矩阵,通过对矩阵谱分解得到的特征值和特征向量表示点的特征,进而计算点的初始匹配概率;其次,利用点对拓扑特征的相似性测度来定义点对间的局部相容性,然后借助概率松弛的方法更新由拟Laplacian谱得到的匹配概率,得出匹配结果。对比实验结果表明,该方法在处理存在噪声和出格点的点集匹配上具有较高的鲁棒性。

基于点对局部拓扑和加权二分图的地面目标关联

针对地面目标探测场景中,目标点集中点的位置包含较多噪声时,传统基于位置信息的目标关联算法的关联准确率下降问题,提出基于点对局部拓扑(PPLT)和加权二分图匹配(WBGM)的地面目标关联方法。该方法采用点对局部拓扑特征刻画目标群中各个成员目标之间的相对位置关系,提高了形状描述算子的抗噪能力。根据全局相似度最大的要求,基于点模式匹配的目标关联问题可以转化为加权二分图匹配问题。最后构建了加权二分图,并通过Kuhn-Munkres算法实现最佳匹配。仿真实验表明,在位置信息存在偏差和目标漏检的情况下,该算法与传统方法相比具有较高的关联正确率和较强的鲁棒性。