Embedded Mutation Strategies in Particle Swarm Optimization to Solve Camera Calibration Problem

Particle swarm optimization is a very effective nature-inspired algorithm which has been widely used in solving many real-world problems over the years. This paper is an attempt to embed different mutation schemes in PSO and testing the different versions of PSO on camera calibration problem. Camera calibration problem is considered as one of the complex real-life problems in the field of image processing. In this paper, the extended particle swarm optimization algorithm is applied and tested for selecting the best parameter value for obtaining minimum pixel length. The results obtained in the empirical section of the paper suggest that PSO has proved to be a better version while embedding different mutation strategies for this problem.