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首页> 外文期刊>Journal of computational and graphical statistics: A joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America >Detecting features in spatial point processes with clutter via local indicators of spatial association
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Detecting features in spatial point processes with clutter via local indicators of spatial association

机译：通过空间关联的局部指标检测空间点过程中的特征

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摘要

We consider the problem of detecting features of general shape in spatial point processes in the presence of substantial clutter. Our goal is to remove clutter from images where one or several features are present and have to be detected. We use a method based on local indicators of spatial association (LISA) functions, particularly on the development of a local version of the product density which is a second-order characteristic of spatial point processes. The classification method is built upon a stochastic version of the EM algorithm (SEM). This method can be applied without user input about the number or shapes of the regions. Our proposal, compared with the kth nearest-neighbor technique, is tested through simulated examples yielding high detection and low false-positive rates. Two real case studies of connective loose tissues in human organs and earthquakes are also presented.
机译：我们考虑在存在大量杂波的情况下在空间点过程中检测一般形状特征的问题。我们的目标是从存在一个或多个特征并且必须进行检测的图像中消除杂波。我们使用一种基于空间关联（LISA）功能的局部指标的方法，特别是基于产品密度的局部版本的开发，这是空间点过程的第二阶特征。分类方法基于EM算法（SEM）的随机版本。可以在无需用户输入有关区域的数量或形状的情况下应用此方法。我们的建议与第k个最近邻技术相比，通过产生高检测率和低假阳性率的模拟示例进行了测试。还介绍了人体器官和地震中结缔组织松动的两个真实案例研究。

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