Image Template Matching Based on Simulated Kalman Filter (SKF) Algorithm

Authors

  • Nurnajmin Qasrina Ann Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang
  • Dwi Pebrianti Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang
  • Zuwairie Ibrahim Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang
  • Luhur Bayuaji Faculty of Computer Science and Software Engineering
  • Mohd Falfazli Mat Jusoh Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang

Keywords:

Image Template Matching, Normalized Crosscorrelation, Optimization, Simulated Kalman Filter,

Abstract

A novel approach to the image matching based on Simulated Kalman Filter (SKF) algorithm has been proposed in this paper. In order, the traditional algorithm to solve image matching problem takes a lot of memory and computational time, image matching problem is assigned to optimization problem and can be solved precisely. The Normalized Cross Correlation (NCC) function of template and sub image is assigned as the fitness function. Experimental results prove that the proposed algorithm is more accurate and precise compared to Particle Swarm Optimization (PSO) algorithm. The percentage of matching result for Cameraman and Mountain are 36% and 32% accordingly which is higher than PSO algorithm, which is 12% and 4% respectively.

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Published

2018-07-04

How to Cite

Ann, N. Q., Pebrianti, D., Ibrahim, Z., Bayuaji, L., & Mat Jusoh, M. F. (2018). Image Template Matching Based on Simulated Kalman Filter (SKF) Algorithm. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-7), 37–41. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4416

Issue

Vol. 10 No. 2-7: Embracing The 4th Industrial Revolution: Innovation in Telecommunication, Electronic & Computer Engineering III

Section

Articles

License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

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