关键词: 图像匹配, 微粒群优化算法, 适应度函数, 混沌

Abstract: For the problems of the image matching are computationally expensive and slow speed and poor robustness，by introducing chaos state into the original Particle Swarm Optimization（PSO），this paper proposes a new algorithm Chaos Particle Swarm Optimization（CPSO）．The new algorithm makes good use of the properties-ergodicity，randomicity，and initial sensitivity of chaos，which realizes non-ergodic searching and can be used to find the best matching point very quickly.At the beginning，the location of the particle is evaluated by chaos.During the running time，chaos perturbation is utilized to avoid the search being trapped in local optimum．CPSO is able to search the global optimizer and avoid the premature convergence on the multidimensional variable space.The experimental results indicate that this approach has high speed and accuracy in image matching and is very effective for image matching processing with noise.

Key words: image matching, particle swarm optimization algorithm, fitness function, chaos