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Detection of Surface Creases in Range Data

  • Alexander Belyaev
  • Elena Anoshkina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3604)

Abstract

We propose a fully automatic and view-independent computational procedure for detecting salient curvature extrema in range data. Our method consists of two major steps: (1) smoothing given range data by applying a nonlinear diffusion of normals with automatic thresholding; (2) using a Canny-like non-maximum suppression and hysteresis thresholding operations for detecting crease pixels. A delicate analysis of curvature extrema properties allows us to make those Canny-like image processing operations orientation-independent. The detected patterns of creases can be considered as ‘shape fingerprints’. The proposed method can be potentially used for shape recognition, quality evaluation, and matching purposes.

Keywords

Range Data Range Image Intersection Curve Positive Maximum Smoothing Procedure 
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 Berlin Heidelberg 2005

Authors and Affiliations

  • Alexander Belyaev
    • 1
  • Elena Anoshkina
    • 1
  1. 1.Max-Planck-Institut für InformatikSaarbrückenGermany

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