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A Comparison of 2-D Moment-Based Description Techniques

  • C. Di Ruberto
  • A. Morgera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

Moment invariants are properties of connected regions in binary images that are invariant to translation, rotation and scale. They are useful because they define a simply calculated set of region properties that can be used for shape classification and part recognition. Orthogonal moment invariants allow for accurate reconstruction of the described shape. Generic Fourier Descriptors yield spectral features and have better retrieval performance due to multi-resolution analysis in both radial and circular directions of the shape. In this paper we first compare various moment-based shape description techniques then we propose a method that, after a previous image partition into classes by morphological features, associates the appropriate technique with each class, i.e. the technique that better recognizes the images of that class. The results clearly demonstrate the effectiveness of this new method regard to described techniques.

Keywords

Zernike Moment Moment Invariant Pattern Recognition Letter Zernike Polynomial Orthogonal Moment 
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

  • C. Di Ruberto
    • 1
  • A. Morgera
    • 1
  1. 1.Dipartimento di Matematica e InformaticaUniversità di CagliariCagliariItaly

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