Weighted Aspect Moment Invariant in Pattern Recognition
- 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
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.
KeywordsPattern Recognition Aspect Moment Invariant Geometric Moment Invariant Weighting Function
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