论文标题
边界的轮廓模型，封闭了星形和大致星形多边形
Contour Models for Boundaries Enclosing Star-Shaped and Approximately Star-Shaped Polygons
论文作者
论文摘要
空间场上的边界将区域与周围背景区域的特定特征分开。这些边界通常用轮廓线描述。为了测量和记录这些边界，轮廓通常表示为连接形成线路的空间点的有序序列。从插值空间场中识别边界线的方法是良好的。人们对如何建模连接空间点的序列的关注更少。对于后一种形式的数据，我们介绍了高斯星形轮廓模型（GSCM）。 GSMC通过从固定起点的各个方向上生成一组距离来生成空间点的序列。 GSCM设计用于建模围绕星形多边形或大约星形多边形区域的轮廓。引入指标以评估多边形偏离星形的程度。仿真研究说明了在各种情况下GSCM的性能，以及对北极海冰边缘轮廓数据的分析突出了如何将GSCM应用于观察数据。
Boundaries on spatial fields divide regions with particular features from surrounding background areas. These boundaries are often described with contour lines. To measure and record these boundaries, contours are often represented as ordered sequences of spatial points that connect to form a line. Methods to identify boundary lines from interpolated spatial fields are well-established. Less attention has been paid to how to model sequences of connected spatial points. For data of the latter form, we introduce the Gaussian Star-shaped Contour Model (GSCM). GSMCs generate sequences of spatial points via generating sets of distances in various directions from a fixed starting point. The GSCM is designed for modeling contours that enclose regions that are star-shaped polygons or approximately star-shaped polygons. Metrics are introduced to assess the extent to which a polygon deviates from star-shaped. Simulation studies illustrate the performance of the GSCM in various scenarios and an analysis of Arctic sea ice edge contour data highlights how GSCMs can be applied to observational data.
