针对空气中极线几何不再适用于水下双目图像匹配以及尺度不变特征转换（SIFT）算法只能实现稀疏匹配的问题，提出了基于色彩分割的水下立体匹配算法。获取双目摄像机标定参数、参考图和待匹配图；推导参考图的特征点在待匹配图上对应的曲线表达式，同时利用均值漂移算法对参考图进行图像分割；用像素灰度差的绝对误差和算法进行匹配，根据图像分割结果对窗口内像素分配不同权重，并将搜索范围改为在对应曲线上，提高匹配精度。实验结果表明，该算法优于特征匹配SIFT算法，匹配精度有所提高，成功将区域匹配算法应用于水下图像稠密匹配。

In term of underwater the epipolar constraint no longer meet binocular image matching, and the scale-invariant feature transform (SIFT) algorithm can only achieve sparse matching, the underwater stereo matching algorithm based on color segmentation is proposed. The calibration parameters of binocular camera are obtained as well as reference image and image to be matched. Corresponding curve expression of the feature points of reference image on image to be matched is derived and the reference image is segmented by mean shift algorithm. The sum of absolute differences algorithm is used to match two images after assigning different weights to the pixels within the window based on the results of image segmentation and searching for the corresponding point in the curve, which can improve the matching accuracy. Experimental results show that the proposed algorithm is superior to feature matching SIFT algorithm, and the matching accuracy is improved. The area matching algorithm is successfully applied to underwater image dense matching.