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PRINCIPAL COMPONENT ANALYSIS -PCA- AND DELONE TRIANGULATIONS FOR PL APPROXIMATION C~1- CONTINUOUS 1-MANIFOLDS IN R~N

机译:R〜N中PL逼近C〜1-连续1-流形的主成分分析-PCA-和delone三角剖分

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A Method is presented which combines statistical (Principal Component Analysis) and deterministic (Voronoi-Delone) methods to find Piecewise Linear approximations of curves C_i(u) in R~3 sampled with statistical noise. If the curves are self-intersecting, there are a finite number of points in which they are not 1-manifolds. Otherwise, they are 1-manifolds in all extents. The combination presented, of PCA and V-D methods, allows the recovery of 1-manifold approximations for C_i (u) for self-intersecting quasi-planar and non self-intersecting curves. In the later case the PCA alone succeeds in finding 1-manifold PL approximations for them. The algorithm implemented finds applications in contour and shape reconstruction from noisy data, subject to sampling errors or blockage.
机译:提出了一种将统计方法(主成分分析)和确定性方法(Voronoi-Delone)相结合的方法,以统计噪声在R〜3中找到曲线C_i(u)的分段线性逼近。如果曲线是自相交的,则有一定数量的点不是1个流形。否则,它们在所有程度上都是1流形。提出的PCA和V-D方法的组合允许恢复自相交的准平面曲线和非自相交曲线的C_i(u)的1流形近似值。在后一种情况下,仅PCA可以成功为它们找到1歧管PL近似值。所实施的算法可从嘈杂的数据中发现轮廓和形状重建中的应用,这些数据容易受到采样误差或阻塞的影响。

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