顾及邻域结构的线状要素Morphing方法

李精忠; 方文江

doi:10.13203/j.whugis20160142

顾及邻域结构的线状要素Morphing方法

李精忠^{1, 2,},
方文江^1, ,

1.
武汉大学资源与环境科学学院, 湖北武汉, 430079
2.
地理信息系统教育部重点实验室(武汉大学), 湖北武汉, 430079

基金项目:

国家重点研发计划 2017YFB0503601

国家重点研发计划 2017YFB0503502

国家自然科学基金 41671448

详细信息

作者简介:
李精忠, 博士, 副教授, 研究方向为DEM分析、地图综合、空间数据多尺度表达。00009232@whu.edu.cn

通讯作者:
方文江, 硕士生。244550975@qq.com

中图分类号: P208;P283
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- HTML全文浏览量: 212
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出版历程
- 收稿日期: 2016-08-02
- 发布日期: 2018-08-04

Morphing Polylines by Preserving Local Neighborhood Structures

LI Jingzhong^{1, 2,},
FANG Wenjiang^1, ,

1.
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
2.
Key Laboratory of Geographic Information System(Wuhan University), Ministry of Education, Wuhan 430079, China

Funds:

The National Key Research and Development Program of China 2017YFB0503601

The National Key Research and Development Program of China 2017YFB0503502

the National Natural Science Foundation of China 41671448

More Information

Author Bio:
LI Jingzhong, PhD, associate professor, specializes in the DEM analysis, map generalization and multiple representations of spatial data. E-mail: 00009232@whu.edu.cn

Corresponding author:
FANG Wenjiang, postgraduate. E-mail: 244550975@qq.com

摘要

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。
- 邻域结构 /
- Morphing /
- 形状上下文 /
- 松弛标记法 /
- 线状要素
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.
- local neighborhood structures /
- Morphing /
- shape context /
- relaxation labeling /
- polylines

HTML全文

图 1 形状上下文计算方法^[10]

Figure 1. Computation of Shape Context^[10]

下载: 全尺寸图片幻灯片

图 2 实验数据和匹配结果

Figure 2. Experimental Data and Matching Results

下载: 全尺寸图片幻灯片

图 3 效果叠加图和局部放大图

Figure 3. Overlaps of the Results and Close-Up of Region IV

下载: 全尺寸图片幻灯片

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