Weighted Central Moment for Pattern Recognition: Derivation, Analysis of Invarianceness, and Simulation Using Letter Characters

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 center-of-mass. To solve this problem, Balslev et.al has modified GMI method by adding a weighting function into GMIpsilas 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 alpha equal to 2/Rg. The experiments reveal that WCM yields intra-class results for identifying picture with different orientations. It also illustrates better inter-class distances in recognizing letter ldquogrdquo and ldquoqrdquo compared to GMI method.