计算机科学
点云
数据挖掘
云计算
聚类分析
点(几何)
功能(生物学)
数学
作者
Linlin Tang,Xupeng Tong,Jingyong Su
出处
期刊:Advances in intelligent systems and computing
日期:2019-11-01
卷期号:: 523-533
标识
DOI:10.1007/978-981-15-3308-2_57
摘要
Classification and recognition of objects for images is an important issue in many scientific fields such as computer vision, biometrics and medical image analysis. An important feature of many objects is shape, so shape analysis has become an important part of classification. One method of shape analysis is to estimate boundaries and analyze the shape of these boundaries to determining the characteristics of the original object. However, many literature studies on point cloud shape analysis are based on existing shapes. This paper mainly refers to Ripley’s K-function in spatial point analysis, through this judgment on spatial distribution of point cloud data to determine the existence of shape in point cloud data, through the spatial distribution of 2D point cloud data and 3D point cloud data. Judging by the randomness of the experiment, K-function has a considerable effect on judging existence of point cloud data shape through relevant experimental verification analysis.
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