An Improved Particle Swarm Optimization Algorithm for Image Matching
Image matching is widely applied in the areas of pattern recognition, computer vision, medicine, remote sensing, aircraft navigation and movement tracking. In this paper, an improved particle swarm optimization algorithm based on variable swarm population size and mutual information as similarity measure function is proposed for image matching. The aim is to enhance the overall performance of image matching. The proposed scheme adjusts the population size in terms of the diversity of the population. The algorithm presented is compared with the exhaustive search based on mutual information, and Standard PSO. Remote sensing images captured by different sensors with different resolutions are as testing data. It is proved that the algorithm the paper suggested is effective for image matching.
particle swarm optimization image matching image registration mutual information variable swarm population size
An Ru Chen Chunye Wang Huilin
Department of Geographical Information Sciences, College of Hydrology and Water Resources, Hohai Uni Institute of Geography and Sea Sciences, Nanjing University, Nanjing 210093, China
国际会议
重庆
英文
7-10
2009-12-25（万方平台首次上网日期，不代表论文的发表时间）