首页> 外文会议>10th Annual European Symposium on Algorithms - ESA 2002, Sep 17-21, 2002, Rome, Italy >Near-Linear Time Approximation Algorithms for Curve Simplification
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Near-Linear Time Approximation Algorithms for Curve Simplification

机译:曲线简化的近线性时间近似算法

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We consider the problem of approximating a polygonal curve P under a given error criterion by another polygonal curve P' whose vertices are a subset of the vertices of P. The goal is to minimize the number of vertices of P' while ensuring that the error between P' and P is below a certain threshold. We consider two fundamentally different error measures ―Hausdorff and Frechet error measures. For both error criteria, we present near-linear time approximation algorithms that, given a parameter ε > 0, compute a simplified polygonal curve P' whose error is less than e and size at most the size of an optimal simplified polygonal curve with error ε/2. We consider monotone curves in the case of Hausdorff error measure and arbitrary curves for the Frechet error measure. We present experimental results demonstrating that our algorithms are simple and fast, and produce close to optimal simplifications in practice.
机译:我们考虑在给定的误差标准下用另一个多边形曲线P'逼近多边形曲线P的问题,该多边形曲线的顶点是P顶点的子集。目标是在确保P'顶点之间的误差最小的情况下最小化P'和P低于某个阈值。我们考虑两种根本不同的误差度量-Hausdorff和Frechet误差度量。对于这两个误差标准,我们提出了近线性时间近似算法,该算法在给定参数ε> 0的情况下,计算一条简化的多边形曲线P'(其误差小于e),且其大小最多为误差为ε的最优简化多边形曲线的大小。 / 2。对于Hausdorff误差测度,我们考虑单调曲线;对于Frechet误差测度,我们考虑任意曲线。我们提供的实验结果表明,我们的算法简单,快速,并且在实践中产生了接近最佳的简化结果。

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