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.