In order to reduce matching error

in this paper

a new matching method for particle images is proposed based on the SOM neural network

which combines the nearest neighbor matching algorithm with the cross correlation algorithm. Firstly

the cross correlation approach is used to evaluate the initial matching position. Secondly

the processing results of the correlation are used to build the neural network. Thirdly

nearest neighbor matching algorithm is adopted to select the best matching points. The modified method can reduce the number of false vectors and improve the practical value. At last

the synthetic particle images and real particle images are tested and the errors are analyzed. The experimental results show that the proposed method is a robust algorithm for measuring the movement of particles and the vector fields can be obtained with high precision.