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海量数据的均匀稀化 被引量:2

Uniform Sparseness of Huge Discrete Data Point
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摘要 为了给后续曲面重构提供合适、均匀分布的数字化型面,从激光测量数据的特点出发,即测点本身趋于均匀分布的情况下,根据古典概率提出了平面数据均匀稀化的4种方案;然后,在曲面局部趋于平面的假设下,又将平面稀化方案扩展到三维空间.由于稀化过程中不需要考虑曲面的具体特征,给实际操作带来了极大的方便.对3个实测数据点的稀化测试,也进一步验证了本文提出的算法是行之有效的. To provide appropriate and uniform digital surface for further surface reconstruction, and proceeding from the feature of laser digitization, that is, measured points trend to be distributed uniformly, four sparse projects for plane data points are presented according to classical probability. Moreover, on the assumption that local surface approaches to a plane,these projects are expanded to 3D spaces. In the process of sparseness,it needn't to consider the surface idiographic features, so it brings out much convenience for practice operation. Tests to three actual measured data also show that the ways provided are effective.
出处 《兰州交通大学学报》 CAS 2006年第4期63-67,共5页 Journal of Lanzhou Jiaotong University
基金 甘肃省自然科学基金资助(ZS031-A25-008-Z) 兰州交通大学首批"青蓝"人才工程资助
关键词 计算机应用 逆向工程 数据预处理 数据稀化 computer application reverse engineering data preprocessing data sparseness
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参考文献5

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二级参考文献12

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