Weighted Aspect Moment Invariant in Pattern Recognition

  • Rela Puteri Pamungkas
  • Siti Mariyam Shamsuddin
Conference paper

DOI: 10.1007/978-3-642-02457-3_66

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5593)
Cite this paper as:
Pamungkas R.P., Shamsuddin S.M. (2009) Weighted Aspect Moment Invariant in Pattern Recognition. In: Gervasi O., Taniar D., Murgante B., Laganà A., Mun Y., Gavrilova M.L. (eds) Computational Science and Its Applications – ICCSA 2009. ICCSA 2009. Lecture Notes in Computer Science, vol 5593. Springer, Berlin, Heidelberg

Abstract

Many drawbacks has been found in Hu’s moment Invariant or known as Geometric Moment Invariant (GMI). Due to its flexibility, GMI is still widely used by the researchers until now. This paper proposes an alternative approach, Weighted Aspect Moment Invariant (WAMI) by combining Weighted Central Moment (WCM) and Aspect Moment Invariant (AsMI) to solve GMI’s drawbacks in term of noise and unequal data scaling. Various insect images are used in this study with two different sizes as simulation images. The simulation results show that the proposed WAMI improves inter-class and intra-class criteria for unequally scaling data compared to AsMI.

Keywords

Pattern Recognition Aspect Moment Invariant Geometric Moment Invariant Weighting Function 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rela Puteri Pamungkas
    • 1
  • Siti Mariyam Shamsuddin
    • 1
  1. 1.Soft Computing Research GroupUniversiti Teknologi Malaysia, SkudaiJohorMalaysia

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