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ISE-bounded polygonal approximation of digital curves

机译:数字曲线的ISE边界多边形逼近

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In this paper we consider a problem of the polygonal approximation of digital curves with a minimum number of approximation segments for a given error bound with L_2-norm. The Integral Square Error bound is defined by the number of vertices in the curve and by constraint on the Root-Mean-Squared-Error (RMSE) of the polygonal approximation. This paper proposes a new, fast and efficient algorithm for solving the problem. The algorithm that is offered was based on searching for the shortest path in a feasibility graph that has been constructed on the vertices of the input curve. The proposed algorithm provides a solution with 97% optimality on average in what is practically real time. This algorithm can also be used in combination with the Reduced-Search Dynamic Programming algorithm as a preliminary step for finding a near-optimal result in an acceptable time. Experiments conducted with the large size vector data have demonstrated both the high degree of efficiency and the fast performance time of the proposed algorithms. These algorithms can be used in practical applications for image vectorization and segmentation, the analysis of shapes and time series, the simplification of vector maps, and the compression of vector data.
机译:在本文中,我们考虑了在给定的误差范围为L_2范数的情况下,具有最小数量的逼近段的数字曲线的多边形逼近问题。积分平方误差范围由曲线中的顶点数和多边形近似的均方根误差(RMSE)的约束定义。本文提出了一种新的,快速有效的算法来解决该问题。提供的算法是基于在可行性图中搜索最短路径的,该可行性图已构建在输入曲线的顶点上。所提出的算法提供了一种在实际实时中平均具有97%最优性的解决方案。该算法还可以与“减少搜索动态规划”算法结合使用,作为在可接受的时间内找到接近最佳结果的预备步骤。使用大尺寸矢量数据进行的实验证明了所提算法的高效性和快速执行时间。这些算法可在实际应用中用于图像矢量化和分割,形状和时间序列的分析,矢量图的简化以及矢量数据的压缩。

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