李精忠, 方文江. 顾及邻域结构的线状要素Morphing方法[J]. 武汉大学学报 ( 信息科学版), 2018, 43(8): 1138-1143. DOI: 10.13203/j.whugis20160142
引用本文: 李精忠, 方文江. 顾及邻域结构的线状要素Morphing方法[J]. 武汉大学学报 ( 信息科学版), 2018, 43(8): 1138-1143. DOI: 10.13203/j.whugis20160142
LI Jingzhong, FANG Wenjiang. Morphing Polylines by Preserving Local Neighborhood Structures[J]. Geomatics and Information Science of Wuhan University, 2018, 43(8): 1138-1143. DOI: 10.13203/j.whugis20160142
Citation: LI Jingzhong, FANG Wenjiang. Morphing Polylines by Preserving Local Neighborhood Structures[J]. Geomatics and Information Science of Wuhan University, 2018, 43(8): 1138-1143. DOI: 10.13203/j.whugis20160142

顾及邻域结构的线状要素Morphing方法

Morphing Polylines by Preserving Local Neighborhood Structures

  • 摘要: 地图综合过程中,综合前后图形轮廓上两点间的绝对距离可能会发生很大改变,但是点的邻域结构和上下文信息相对保持稳定。基于此,首先提出一种结合形状上下文和松弛标记法的形状匹配方法,通过全局形状描述子形状上下文来描述点集的不变特征;然后将点集间形状上下文的统计检验匹配代价转化为松弛标记法的初始匹配概率,接着通过迭代支持度函数更新匹配概率,直到建立最优匹配;最后根据点集的匹配关系,得到相应的匹配线段,通过线性插值实现要素的连续尺度变换。实验结果表明,该方法不仅能够很好地顾及要素的上下文信息,而且也能顾及到邻域结构特征,提高Morphing变换的精度。

     

    Abstract: This paper presents a Morphing method of polylines based on shape matching by preserving local neighborhood structures. Although the absolute distance between two points may change significantly during the map generalization, the global context and the neighborhood structure of points are generally well preserved and more stable. We first introduce a shape matching method by combining the shape context and relaxation labeling which takes advantages of the global context and local neighborhood structure. Using shape context descriptors, we get the matching costs between points which can be used to initialize the matching probability matrix during relaxation labeling. Afterwards, weiterate the support functions to update the matching probability matrix until we get the optimal match-ing results. The two polylines are divided into two groups of sub-segments by the matching results. Finally, by using the linear interpolation method, we make Morphing for every pair of the corresponding sub-segments. Extensive experiments have shown that our method can well preserve both the global context and local neighborhood structures, and can improve the accuracy of Morphing transformation.

     

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