The Study on Particle Image Velocimetry Based on SOM Network
自组织映射神经网络在粒子图像匹配中的研究

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.