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基于曲率的主分量分析及应用

Curvature-based Principal Component Analysis and Application
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摘要 提出一种基于曲率的主分量分析算法。该算法先计算各点的曲率,包括主曲率、高斯曲率和平均曲率,并将点云数据映射到曲率空间,对曲率空间进行主分量提取,然后对点云数据进行压缩。实验结果表明,该算法具有较好的压缩性能。将PCA算法引入三维点云数据压缩,弥补了传统PCA方法的缺陷。 A primary component analysis algorithm on account of curvature is propsed to compress the data of 3D point cloud.The primary curvature,Gaussian curvature and average curvature of each point is calculated.The point cloud data is mapped to the curvature space to extract the primary component of the curvature space.The point cloud data is compressed.Test results show that this algorithm has a perfect compression performance.The innovation of the paper is that PCA algorithm is introduced into compression of 3D point cloud data.The algorithm conquered the disadvantages of the traditional PCA method.
出处 《现代电子技术》 2011年第22期1-3,10,共4页 Modern Electronics Technique
基金 国家自然科学基金资助项目(60963025) 海南省自然科学基金资助项目(610230)
关键词 曲率 主分量分析 数据压缩 三维数据 curvature PCA(primary component analysis) data compression 3D data
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参考文献8

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