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Weighted aspect moment invariant in pattern recognition

Pamungkas, Rela Puteri and Shamsuddin, Siti Mariyam (2009) Weighted aspect moment invariant in pattern recognition. In: Computational Science and Its Applications – ICCSA 2009. Springer Verlag, Germany, pp. 806-818. ISBN 978-364202456-6

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Official URL: http://dx.doi.org/10.1007/978-3-642-02457-3_66

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.

Item Type:Book Section
Additional Information:International Conference on Computational Science and Its Applications, ICCSA 2009; Seoul; 29 June 2009 through 2 July 2009
Uncontrolled Keywords:aspect moment invariant, geometric moment invariant, pattern recognition, weighting function
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:13193
Deposited By: Liza Porijo
Deposited On:22 Jul 2011 01:59
Last Modified:29 Apr 2013 07:44

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