A robust scheme of model parameters estimation based on the particle swarm method in the image matching problem
Alexey Chernyavskiy · Journal of Computer and Systems Sciences International · 2008
A new iterative method for robust estimation of image transform parameters based on particle swarm optimization is proposed. The main distinction of the method from the RANSAC method of random search which is frequently applied to solving problems of robust parameters estimation in computer vision problems, consists in the fact that at each iteration the test samples are generated with taking into account the information about model quality, constructed based on samples at all previous iterations, rather than randomly. The rules for refinement of samples are motivated by the behavior of swarm (schooling) living creatures. The efficiency of the new SwarmSAC algorithm is illustrated by an example of stereo matching of two images when matching errors (outliers) are present. The results of comparison of the algorithm with the RANSAC method demonstrate the advantage of the new algorithm in solving the image matching problem. The new method is generic and can be applied to various problems of robust parameters estimation of parameters and filtering of outliers.