基于多尺度张量分解的点云结构特征提取  

Structural Feature Extraction from Point Clouds Based on Multi-scale Tensor Decomposition

作　　者：林洪彬[1] 刘彬[1] 张玉存[1] 

机构地区：[1]燕山大学河北省测试计量技术与仪器重点实验室,秦皇岛066004

出　　处：《中国机械工程》2012年第15期1833-1839,共7页China Mechanical Engineering

基　　金：国家科技重大专项(2011ZX04002-101);河北省自然科学基金资助项目(E2012203002);河北省科学技术研究与发展计划资助项目(10212152);秦皇岛市科学技术研究与发展计划资助项目(201001A077);河北省重点实验室开放基金资助项目

摘　　要：针对传统点云处理算法弱特征提取与抗噪声能力之间的矛盾,提出了一种基于多尺度张量分解的点云结构特征提取算法。首先,利用张量矩阵奇异值分解进行采样点特征显著性编码;然后,将法向(切向)一致性测度与罗曼诺夫斯基准则相结合求取采样点最优邻域,以提高采样点特征识别的可靠性;最后,利用最小生成森林进行特征点遍历,构建点云结构特征曲线。实验结果表明,该算法可以实现复杂点云结构特征的有效识别。To solve the conflicts between the ability of weak feature extraction and noise resistency of traditional algorithms, a new feature extraction algorithm was proposed based on multi--scale ten- sor decomposition. Firstly, feature saliency encoding was defined based on the singular value decom- position of tensor matrix. Secondly, normal（tangential） consistent measure was constructed and used to determine the maxmuim scale combined with Romanovskii criterion. The reliability of the feature reconizing algorithm is improved. Finaly, the feature lines were constructed using minimal spanning forest. Expremental results reveal the weak feature extraction and noise resistency abilities of the method.

关 键 词：多尺度分析 张量分解 点云 特征提取 

分 类 号：TN957.52[电子电信—信号与信息处理]