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基于对称Kullback-Leibler散度的点集配准方法

Point Set Registration Method Based on Symmetric Kullback-Leibler Divergence

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摘要

提出一种基于SKL (Symmetric Kullback-Leibler)散度的点集配准算法,将点集中的每一个点表示成一个高斯分布,高斯分布包含点的位置信息和周围点的影响大小信息。将整个点集建模为一个高斯混合模型(GMM),因此两点集的配准问题转化为求两GMM间SKL散度的最小值问题。采用遗传算法进行优化求解。实验结果表明,所提算法对噪声、出格点和缺失点具有较强的鲁棒性,且取得较高的配准精度。

Abstract

A point set registration algorithm based on symmetric Kullback-Leibler (SKL) divergence is proposed. Each point in the point set is represented as a Gaussian distribution. The Gaussian distribution includes the location information of the point and the influences from surrounding points. The whole point set is modeled as a Gaussian mixture model (GMM). The registration problem of two point sets is thus formulated as the minimum value solution of SKL divergence between two GMMs. The genetic algorithm is used for optimal solution. The experimental results show that the proposed algorithm is robust to noise, outliers, and missing points, and achieves good registration accuracy.

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中图分类号:TP753

DOI:10.3788/LOP57.081022

所属栏目:图像处理

基金项目:国家自然科学基金、安康学院高层次人才项目、安康市科技计划项目;

收稿日期:2019-07-11

修改稿日期:2019-09-24

网络出版日期:2020-04-01

作者单位    点击查看

杨小艳:安康学院电子与信息工程学院电子信息技术研究中心, 陕西 安康, 725000

联系人作者:杨小艳(lotus_summer117@163.com)

备注:国家自然科学基金、安康学院高层次人才项目、安康市科技计划项目;

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引用该论文

Yang Xiaoyan. Point Set Registration Method Based on Symmetric Kullback-Leibler Divergence[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081022

杨小艳. 基于对称Kullback-Leibler散度的点集配准方法[J]. 激光与光电子学进展, 2020, 57(8): 081022

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