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A Multiresolution Approach for Segmenting Surfaces

  • G. G. Pieroni
  • S. P. Tripathy
Part of the International Centre for Mechanical Sciences book series (CISM, volume 307)

Abstract

Automatic analysis of surface morphology is relevant to several application areas in robotic vision and biomedical research. Depending on the representation of a surface, a number of methods are presently available in order to perform segmentation of a surface into meaningful regions. In this work, we assume that the surface is a well defined compound of triangular tiles. The segmentation is performed by using the discrete approximation of the Gaussian and Mean curvatures. According to the rules of differential geometry the signs of the two operators determine the local differential properties such that a classification of a point into one of eight possible classes can be performed. In this work, a similar technique has been developed by using triangular tiles instead of points. When noisy surfaces are considered, the use of differential geometry loses meaning. In order to overcome this difficulty a novel technique, which takes advantage of multi-resolution analysis, has been developed. Experiments were performed to decompose several geometric surfaces. The geometric surfaces that were used were those that could be expressed as explicit functions of x and y. Noiseless and noisy surfaces were tested.

Keywords

Fundamental Form Gaussian Curvature Principal Curvature Normal Curvature Noisy Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 1989

Authors and Affiliations

  • G. G. Pieroni
    • 1
  • S. P. Tripathy
    • 2
  1. 1.University of UdineUdineItaly
  2. 2.University of HustonHustonUSA

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