Template Matching Algorithm Based on New Particle Swarm Optimization

A new image matching algorithm based on particle swarm optimization (PSO) is proposed to solve the problems of falling into local optimum, slow search speed and low matching accuracy in the process of image matching. Firstly, the particle swarm optimization algorithm is optimized with improved nonlinear inertial weight to balance the global and local searching ability of particles. Secondly, the adaptive learning factor is improved to further enhance the local search ability, so that the particle swarm can obtain better search ability at different stages, and avoid the particle velocity stagnation to zero in the later stage of the algorithm and fall into the local optimal. Simulation results show that the proposed algorithm improves the global searching ability and convergence accuracy of particles, and effectively prevents premature convergence. Compared with the PSO image matching algorithm based on standard, the proposed algorithm has the characteristics of fast convergence, good robustness and high matching accuracy.