在基于区域的立体匹配中，由于遮掩、区域变形及光照条件会对匹配算法造成很大的影响，而传统的顺序性约束、唯一性约束、外极线约束和邻域约束并不能很好地解决这些问题，而近几年提出的相对位置约束虽能解决其中大部分问题，但对于区域的遮掩情况依然效果不佳。为此提出了一种新的基于Zernike矩的区域匹配算法，该算法在相对位置约束的基础上，采用中心距离和Ｚernike矩构造了新的费用函数，并提出根据匹配区域之间中心距离的大小来动态评判费用函数的权重系数值，从而提高了算法的性能。实验结果表明，该算法优于原方法，且对于区域的遮掩和变形情况都具备更好的识别性能，是一种行之有效的区域匹配算法。

For region matching in stereoscopic images, most algorithms will be affected by the factors such as regions occlusion, regions warping and lighting condition.So the traditional constraint conditions, like order constraint, unique constraint, epipolar constraint and adjacent constraint, may be violated by these cases. In past few years a new algorithm based on the relative position constraint (RPC) between regions is proposed which can overcome most of the problems mentioned above, but it has not satisfying performance in matching occluded objects in the stereo images. Therefore, there remain false matches and miss correspondences in the final results. In this paper, a novel algorithm using both the relative position constraint and the new cost function on the basis of regions center distance and Zernike moments theory is proposed. Furthermore the adjustable weights of cost function are dynamically estimated according to the distance between the centers of two matching regions. Finally, the proposed region matching algorithm is illustrated by three synthesized stereo images, with a comparison to the present algorithm and the superiority of the new region matching algorithm over the present algorithm is experimentally verified.