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An automatic classification and robust segmentation procedure of spatial objects

机译：空间物体的自动分类和鲁棒分割程序

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摘要

This paper proposes a statistical procedure for the automatic volumetric primitives classification and segmentation of 3D objects surveyed with high density laser scanning range measurements. The procedure is carried out in three main phases: first, a Taylor's expansion nonparametric model is applied to study the differential local properties of the surface so to classify and identify homogeneous point clusters. Classification is based on the study of the surface Gaussian and mean curvature, computed for each point from estimated differential parameters of the Taylor's formula extended to second order terms. The geometrical primitives are classified into the following basic types: elliptic, hyperbolic, parabolic and planar. The last phase corresponds to a parametric regression applied to perform a robust segmentation of the various primitives. A Simultaneous AutoRegressive model is applied to define the trend surface for each geometric feature, and a Forward Search procedure puts in evidence outliers or clusters of non stationary data.
机译：本文提出了一种统计程序，用于通过高密度激光扫描范围测量来测量的3D对象的自动体积基元分类和分割。该过程分为三个主要阶段：首先，使用泰勒展开非参数模型研究表面的微分局部特性，从而对均质点簇进行分类和识别。分类基于对表面高斯和平均曲率的研究，根据扩展到二阶项的泰勒公式的估计微分参数为每个点计算得出。几何图元分为以下基本类型：椭圆形，双曲形，抛物线形和平面形。最后一个阶段对应于用于执行各种图元的鲁棒分割的参数回归。应用同时自动回归模型来定义每个几何特征的趋势面，并且正向搜索过程会放入证据异常值或非平稳数据簇。

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