Pamungkas, Rela Puteri and Shamsuddin, Siti Mariyam (2009) Weighted central moment for pattern recognition: derivation, analysis of invarianceness, and simulation using letter characters. In: 2009 Third Asia International Conference on Modelling & Simulation. Institute of Electrical and Electronics Engineers, New York, pp. 102-106. ISBN 978-076953648-4
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Official URL: http://dx.doi.org/10.1109/AMS.2009.124
Geometric Moment Invariant (GMI) is well known approach in pattern recognition. One of the weaknesses of GMI is in its invarianceness, where data or points concentrated near to the center-of-mass are neglected because of the existence of data or points that are far away from the centerof- mass. To solve this problem, Balslev et.al has modified GMI method by adding a weighting function into GMI's formula; thus we called it as Weighted Central Moment (WCM). WCM can increase noise tolerance for rotation/translation independent pattern recognition. In this paper, we present simulation results for characters with adjustable parameter a equal to 2/Rg. The experiments reveal that WCM yields intraclass results for identifying picture with different orientations. It also illustrates better inter-class distances in recognizing letter "g" and "q" compared to GMI method.
|Item Type:||Book Section|
|Additional Information:||2009 3rd Asia International Conference on Modelling and Simulation, AMS 2009; Bandung, Bali; 25 May 2009 through 26 May 2009|
|Uncontrolled Keywords:||central moment, component, geometric moment invariant, inter-class, intra-class, lorentzian function, weighted central moment|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Computer Science and Information System|
|Deposited By:||Liza Porijo|
|Deposited On:||22 Jul 2011 01:54|
|Last Modified:||22 Jul 2011 01:54|
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