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An automatic classification and robust segmentation procedure of spatial objects

Author

Listed:
  • Fabio Crosilla

    (University of Udine)

  • Domenico Visintini

    (University of Udine)

  • Francesco Sepic

    (University of Udine)

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.

Suggested Citation

  • Fabio Crosilla & Domenico Visintini & Francesco Sepic, 2007. "An automatic classification and robust segmentation procedure of spatial objects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(3), pages 329-341, February.
    • Handle: RePEc:spr:stmapp:v:15:y:2007:i:3:d:10.1007_s10260-006-0033-5
    DOI: 10.1007/s10260-006-0033-5
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    References listed on IDEAS

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    1. Tonino Sclocco & Marco Marzio, 2004. "A weighted polynomial regression method for local fitting of spatial data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 13(3), pages 315-325, December.
    2. Andrea Cerioli & Marco Riani, 2002. "Robust methods for the analysis of spatially autocorrelated data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(3), pages 335-358, October.
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