An automatic classification and robust segmentation procedure of spatial objects

Fabio Crosilla (), Domenico Visintini and Francesco Sepic
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Fabio Crosilla: University of Udine
Domenico Visintini: University of Udine
Francesco Sepic: University of Udine

Statistical Methods & Applications, 2007, vol. 15, issue 3, No 4, 329-341

Abstract: Abstract 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.

Keywords: Spatial objects; Combined segmentation and classification; Parametric and nonparametric regression models; Forward search (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/s10260-006-0033-5

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