Quasi-dense Correspondence in Stereo Images Using Multiple Coupled Snakes

In this paper, we present a new method to establish quasi-dense correspondence between a pair of stereo images without camera calibration. Our proposed method is based on the traditional snake formulation using an energy function. The energy function incorporates a new matching term. When the energy function is minimized, the control points along the curves in a stereo pair are matched. Moreover, a penalty term is applied to prevent two snakes in the same image to overlap. In our method, snakes in both images are coupled and can change their shapes simultaneously. In particular, the control points on the curves are matched and evolved at the same time. Comparing our method to the conventional stereo methods, the latter requires camera parameters in order to employ the epipolar constraint to locate correspondences while ours does not. Comparing to the traditional feature-based stereo methods such as those using SIFT and SURF, the number of correspondences established by our method is significantly higher. Our method is especially suitable in scenes for which there are many textureless regions, and hence SIFT/SURF can find few matches. In order to evaluate the accuracy of different methods, the fundamental matrix is computed using the correspondences established by each method. The experimental results from both synthetic and real images are compared to the ground truth and to the conventional sparse matching method to demonstrate that our method has significant improvement over existing methods.