By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Pamungkas, R.P. ; Soft Comput. Res. Group, Univ. Teknol. Malaysia, Skudai ; Shamsuddin, S.M.

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 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.

Published in:

Modelling & Simulation, 2009. AMS '09. Third Asia International Conference on

Date of Conference:

25-29 May 2009