融合空间聚类与结构特征的点集配准优化算法

Point set registration optimization algorithm using spatial clustering and structural features

在点集配准中,噪声、非刚性形变和误匹配的存在,产生了求解非线性最优空间变换困难的问题。针对这个问题引入局部约束条件,提出了一种采用局部空间聚类和邻域结构特征的点集配准优化算法(PR-SDCLS)。首先,利用点集空间距离矩阵构造运动一致性聚类子集和离群值聚类子集;然后,在运动一致性聚类子集中分别使用高斯混合模型拟合,并引入通过融合形状上下文特征描述子与加权空间距离获得考虑全局和局部特征的混合系数;最后,采用最大期望算法完成参数估计,实现了混合模型的非刚性点集配准模型;为了提高算法效率,模型变换采用再生核希尔伯特空间建模,并使用核近似策略。实验结果表明,该算法在涉及不同类型数据退化(变形、噪声、离群点、遮挡和旋转)的非刚性数据集上,面对大量异常值时具有良好的配准效果和鲁棒性,配准平均误差的均值在经典和先进的算法基础上降低了约42.0538%。

The existence of noise,non-rigid deformation and mis-matching in point set registration results in the difficulty of solving nonlinear optimal space transformation.This paper introduces local constraints and proposes a point set registration optimization algorithm using spatial distance clustering and local structural features(PR-SDCLS).First,the motion consistency clustering subset and outlier clustering subset are constructed by using the point set space distance matrix;Then,the Gaussian mixture model is used to fit the motion consistency cluster subset,and the mixing coefficient considering global and local features is obtained by fusing the shape context feature descriptor and weighted spatial distance.Finally,the maximum expectation algorithm is used to complete the parameter estimation,and the non-rigid point set registration model of the Gaussian mixture model is realized.In order to improve the efficiency of the algorithm,the model transformation uses the reproducing kernel Hilbert space model,and uses the kernel approximation strategy.Experimental results show that the algorithm has a good registration effect and robustness in the face of a large number of outliers on non-rigid data sets involving different types of data degradation(deformation,noise,outliers,occlusion and rotation),and the mean value of registration average error is reduced by 42.0538%on the basis of classic and advanced algorithms.