Point Pattern Matching (PPM) is an important issue of computer vision and pattern recognition

which is widely used in target recognition

medical and remote image registration

pose estimation

etc. This paper proposes a particle swarm optimization (PSO) based approach for pose and correspondences estimation between the feature points of two images under affine transformation. In the method

the point sets matching problem is formulated as an objective function’s optimization problem in the affine transformation parameters solution space. The PSO is used to search for optimal transformation parameters. There are three contributions made in this paper. Firstly

we develop an initial transformation parameters estimation method for PSO

which greatly improve the algorithms efficiency and veracity. Secondly

we introduce a threshold to correspondence finding

which rejects outliers and enhances veracity while using “Nearest Neighbors Search”. Thirdly

we propose two approaches to improve the searching efficiency when using the original PSO. Experiments demonstrate the validity and robustness of the algorithm.