Feature point matching of curved surface and robust uncertainty

Stereo matching is an important research area in stereovision and stereo matching of curved surface is especially crucial. A novel correspondence algorithm is presented and its matching uncertainty is computed robustly for feature points of curved surface. The corners are matched by using homography constraint besides epipolar constraint to solve the occlusion problem. The uncertainty sources are analyzed. A cost function is established and acts as an optimal rule to compute the matching uncertainty. An adaptive scheme Gauss weights are put forward to make the matching results robust to noises. It makes the practical application of corner matching possible. From the experimental results of an image pair of curved surface it is shown that computing uncertainty robustly can restrain the affection caused by noises to the matching precision.