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2-D Shape Matching Using Asymmetric Wavelet-Based Dissimilarity Measure

  • Ibrahim El Rube’
  • Mohamed Kamel
  • Maher Ahmed
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)

Abstract

In this paper, a wavelet-based multiscale asymmetric dissimilarity measure for shape matching is proposed. The wavelet transform is used to decompose the shape boundary into a multiscale representation. Given two shapes, a distance matrix is computed from the moment invariants of the wavelet coefficients at all the scale levels. The asymmetric dissimilarity is then calculated from the minimum values across each row on the distance matrix. The proposed asymmetric dissimilarity is a Hausdorff-like measure and is used for finding globally related shapes. The similarity paths obtained from the locations of the minimum distance values can be used to illustrate these relations.

Keywords

Distance Matrix Wavelet Decomposition Scale Level Dissimilarity Measure Shape Match 
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 2004

Authors and Affiliations

  • Ibrahim El Rube’
    • 1
  • Mohamed Kamel
    • 2
  • Maher Ahmed
    • 3
  1. 1.Systems Design EngineeringUniversity of WaterlooCanada
  2. 2.Electrical and Computer EngineeringUniversity of WaterlooCanada
  3. 3.Physics and Computer Science DepartmentWilfrid Laurier UniversityCanada

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