立体视觉一直是计算机视觉领域所研究的一个中心问题，而立体匹配则是立体视觉技术中最关键也是最困难的部分，就得到适用于基于图象绘制技术中视图合成的准确、高密度视差图（Disparity Map）而言，现有的一些方法存在一定的局限性。考虑到立体匹配过程中存在的不确定性和模糊性，本文将已获得广泛应用的模糊理论引入立体匹配领域，提出了基于模糊判别的立体匹配算法，并用实际图象与合成图象进行了实验验证，结果表明该算法效果良好，具有实用价值。

Stereo vision has long been one of the central research problems in computer vision, and stereo matching is the most important and difficulty issue of stereo vision. There are some limits for existing approaches to recover precise and dense disparity map. Feature-based stereo can produce more precise matching but only sparse disparity map. On the other hand, Area-based approaches can provide dense disparity map but less precise matching. In the situation of image synthesis for IBR, we need not only precise matching but also a dense disparity map. Thinking of the uncertain and fuzzy characteristic during matching, we introduce the widely used fuzzy set theory to the field of stereo matching, and propose an algorithm based on fuzzy identification. The algorithm uses the information of gradient magnitudes, angles of orientation and gray value information of nearby points as the identification facts. Experiments with real and synthetic images have been performed, they show that this algorithm is effective and it is of great value to use.