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An application of the c-varieties clustering algorithms to polygonal curve fitting

机译:c变量聚类算法在多边形曲线拟合中的应用

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摘要

An algorithm is described that fits boundary data of planar shapes in either rectangular coordinate or chain-coded format with a set of straight line segments. The algorithm combines a new vertex detection method, which locates initial vertices and segments in the data, with the c-elliptotype clustering algorithm, which iteratively adjusts the location of these initial segments, thereby obtaining a best polygonal fit for the data in the mean-squared error sense. Several numerical examples are given to exemplify the implementation and utility of this new approach.
机译:描述了一种算法,该算法将直角坐标或链编码格式的平面形状的边界数据与一组直线段拟合。该算法结合了一种新的顶点检测方法(可在数据中定位初始顶点和线段)和c-椭圆型聚类算法,该算法可迭代地调整这些初始线段的位置,从而在均值中获得数据的最佳多边形拟合-平方误差感。给出了几个数值示例来说明这种新方法的实现和实用性。

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