摘要
针对点模型提出了基于前向查找和均值漂移两种鲁棒统计方法的滤波算法。前向查找算法根据残差图自动检测离群点,并将输入的点云数据划分为多个不带离群点的最优局部降噪邻域。对局部邻域进行加权协方差分析,估计出该邻域的最小二乘拟合平面。在局部邻域内估计采样点的核密度函数并通过均值漂移算法计算它的局部最大值点,核密度函数的局部最大值点确定了点云数据的聚类中心并能准确逼近采样点曲面,将每一个采样点漂移到密度函数的局部最大值点,使点云曲面收敛为一个稳定的三维数字模型。实验结果表明,本文的算法是鲁棒的,能在有效剔除点模型表面噪声的同时较好地保持模型表面的尖锐特征。
Based on two robust statistics methods, forward-search and mean-shlft, an algorithm for robust filtering of noisy point-sampled models was presented. Forward-search algorithm detected outliers automatically by using residual plot and classified point clouds to multiple optimal outlier-free neighborhoods locally. By analyzing the weighted covariance matrix of a local neighborhood, its least-squares plane was estimated. Kernel functions of sample points in local regions were estimated and the local maxima of the kernels was computed by using mean-shift technique. The local maxima of the kernel estimation function determined cluster centers of point cloud data, which delivered an accurate approximation of the sampled surface. Each sample point was shifted to the local maximum of the kernel function, so the point-set surface could converge to a stable 3D digital model. Experiments show that our method is robust. It can smooth the noise efficiently and preserve the sharp features of the surface effectively.
出处
《计算机应用》
CSCD
北大核心
2006年第3期582-585,共4页
journal of Computer Applications
基金
铁道部科技研究开发项目(2003X040-A)
关键词
前向查找算法
均值漂移算法
协方差分析
非参数核密度估计
离群点
forward-search algorithm
mean-shift algorithm
covariance analysis
nonparametric kernel densityestimation
outlier
