立体匹配是计算机视觉研究的经典难题，其算法的复杂度和精度直接影响了视觉系统对外部景物的重建性能。为此提出了一种新的基于神经网络的立体匹配方法，其基本思想是：在实现核线重排的前提下，利用唯一性、相容性以及相似性等匹配约束条件，建立反映对应极线间所有匹配点约束关系的能量函数，将其映射到二维Hopfield网络进行极小化求解，网络最后的稳态表示匹配点的对应关系；通过对图中所有极线进行上述操作，可以得到所求的视差图。与传统方法相比，本算法具有两个明显的特点：(1)匹配基元采用了普通的图像点，可以直接获得稠密的深度图；(2)Hopfield网的外部输入不再为常数，而是一个反映对应点灰度相似性关系的值。通过对合成图以及真实图景进行测试，验证了该方法的有效性。

Stereo matching is one of the classic difficult problems in the computer vision, and its complexity and precision hedge the capability of vision system to reconstruct the 3-D scene. This paper presents a new matching method based on neural network. On the condition that stereo rectification has been performed, the energy function is built on the basis of uniqueness, compatibility and similarity constraints, which reflects the constraint relations of all matching units of the same lines. It is then mapped onto a 2-D neural network for minimization, whose final stable state indicates the possible correspondence of the matching units. The depth map can be acquired through performing the above operation on the all epipolar lines. The algorithm has two traits relative to the traditional approach. 1. Individual pixel point but not scene point or edge line is adopted as matching unit and dense depth map could be obtained directly. 2. The external input of the nodes is not constant again and is the function of gray similarity of correspondent points. The experiments on the synthetic and real images demonstrate the feasibility of our approach.