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基于K-近邻点云去噪算法的研究与改进
引用本文:张毅,刘旭敏,隋颖,关永.基于K-近邻点云去噪算法的研究与改进[J].计算机应用,2009,29(4):1011-1014.
作者姓名:张毅  刘旭敏  隋颖  关永
作者单位:首都师范大学 首都师范大学信息工程学院 首都师范大学
基金项目:国家自然科学基金,北京市教育委员会科技发展计划重点项目,北京市自然科学基金 
摘    要:针对三维扫描获取的带噪声和离群点的点云数据,提出了改进的去噪算法。通过K-近邻搜索建立散乱点云之间的拓扑关系,进而采用高斯影响函数作为核函数来估计当前测点对周围邻近点的影响力,从而限制噪声并剔除离群点。重点讨论了密度熵的概念以及如何优化高斯核函数的参数,解决了窗宽尺寸在使用中不易确定的问题。仿真实验证明,该算法能够很容易地检测出离群点,同时也避免了将模型上的点误判为离群点的问题。

关 键 词:K-近邻    离群点    高斯核函数    密度熵    去噪
收稿时间:2008-10-10
修稿时间:2008-11-27

Research and improvement of denoising method based on K-neighbors
ZHANG Yi,LIU Xu-min,SUI Ying,GUAN Yong.Research and improvement of denoising method based on K-neighbors[J].journal of Computer Applications,2009,29(4):1011-1014.
Authors:ZHANG Yi  LIU Xu-min  SUI Ying  GUAN Yong
Affiliation:1.College of Information Engineering;Capital Normal University;Beijing 100048;China;2.Institute of Command Automation;PLA University of Science and Technology;Nanjing Jiangsu 210007;China
Abstract:An improved method for denoising the point clouds with noises and outliers acquired by a 3D scanner was presented. The method established the topology connection of the scattered points by searching the K-neighbors of each point. The Gaussian function was used as a kernel function to estimate the current point's effect on its neighbors, so the noises could be restricted and the outliers could be removed. The concept of density entropy and how to optimize the parameter of Gaussian function are the emphases. The method solves the problem of window-width's uncertainty in application. The results of emulation experiments show that the method can detect outliers easily, and it avoids mistaking points on the model as outliers.
Keywords:K-neighbors  outliers  Gaussian kernel function  density entropy  denoise
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