基于对现有图割算法的研究，设计了基于自适应分水岭算法并使用非参数深度平滑模型来建立图割能量方程的立体匹配方法。提出了新的自适应局部阈值方法，并将其应用于分水岭结合Prim算法的区域融合中。该方法选取相同亮度的像素作为同一个特征矢量形成像素组层，这样两幅或多幅图像的匹配可以在特征区域像素组层来计算，大大减少了数据量。在最小化能量方程时，基于像素组层优化现有的α扩展算法，降低运行时间。通过Middlebury测试平台对算法定量评估得出在所有区域的误匹配率、非遮挡区域以及深度不连续区域的误匹配率都控制在8.5%以内，在Middlebury测试平台135组数据中排名第19位。

Based on the existing graph cut algorithms, a stereo matching method which is based on adaptive watershed algorithm and non-parametric depth smoothing model to create an energy equation of graph cut is designed. A novel adaptive local threshold method is proposed, which is applied to region integration by combining the watershed with Prim′s algorithm. The proposed method selects the pixels with same brightness as a feature vector and forms pixels group, thus two or more images can be matched in the pixels group layer of feature region, then the amount of data can be greatly reduced. When the energy equation is minimized, pixels groups layer can be used to optimize the α-expansion algorithm, and reduce the running time. Experimental results indicate that the error match ratios of all regions, non-occluded area and depth discontinuity regions are all less than 8.5%. The proposed algorithm is in the place of 19 among all the 135 algorithms in Middlebury testing platform.