利用神经网络优化技术解决图象序列的特征点匹配问题，将特征点匹配归结为一个带约束的优化问题，并用2D Hopfield网络实现，在Hopfield网络的能量函数的设计中，综合考虑了特征点的预测结果、特征点的遮挡等情况，从而克服了现有的多数方法所存在的误匹配现象，对于特征点的跟踪，头3帧图象的正确匹配是十分关键的。本文提出了一种3D Hopfield网络用以解决头3帧图象的特征点匹配，并提出了一个运动平滑性的代价函数用以构造3D Hopfield网络的能量函数，实际图象序列的实验结果证明了本方法的有效性。

This paper proposes an approach to feature point correspondence of image sequence based on neural networks. We formulate the correspondence problem as a constrained optimization problem and propose a 2D Hopfield neural network to solve it. The design of energy function of neural network has ranged over the results of visual tracking and the condition of occlusion. Therefore, it can solve the problem of error correspondence resulting from current existing methods. The correct correspondence of the first three frames is very important for the point tracking. This paper develops a 3D Hopfield network to handle the correspondence of the first three frames and proposes a cost function of motion smoothness to formulate the energy function of 3D Hopfield network. Experiment on a real image sequence demonstrates the feasibility of the approach.