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

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 take a lot of memory and computational time, image matching problem is assigned to optimization problem and can be solve 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 are 12% and 4% respectively