The method is a 2.5D surface modelling technique which is based on the application of statistical nonparametric regression methods for point cloud regularization and mesh smoothing, specifically the kernel-smoothing techniques.
Results from a previous work where the subject of study are point clouds representing urban landscapes suggested that hybridization of current methods for point cloud segmentation perform better if compared to the methods themselves.
Clustering methods for point in spaces has been the subject of many studies and are used in many different fields.
To analyze such clustering patterns systematically, statistical methods for point pattern analysis can be employed.
New and simpler methods for point of care testing have evolved to the extent that they appear to be highly accurate within the critical care setting [ 2, 9].
However, the basic principle is applicable also to other designs such as case-control studies so long as methods for point and interval estimation of RD, PAR, and AFe are available.
