Matching features computed in images is an important process in multiview image analysis. When the motion between two images is large, the matching problem becomes very difficult. In this paper, we propose a contour matching algorithm based on geometric constraints. With the assumption that the contours are obtained from images taken from a moving camera with static scenes, we apply the epipolar constraint between two sets of contours and compute the corresponding points on the contours. From the initial epipolar constraints obtained from corner point matching, candidate contours are selected according to the epipolar geometry, contour end point constraints, and contour distance measures. In order to reduce the possibility of false matches, the number of match points on a contour is also used as a selection measure. The initial epipolar constraint is refined from the matched sets of contours. The algorithm can be applied to a pair or two pairs of images. All of the processes are fully automatic and successfully implemented and tested with various real images.

We present a novel method of velocity field estimation for points on moving contours in an image sequence. The method determines the corresponding point in the next image frame by considering curvature changes at each point on a contour. In previous methods, there are errors in estimation for the points which have low curvature variations since those methods compute the solutions by approximating the normal component of optical flow. The proposed method computes optical flow vectors of contour points by minimizing the curvature changes. As a first step, snakes are used to locate smooth curves in 2D imagery. Then, the extracted curves are tracked continuously. We excluded the rearranging process in snakes and allowed the snaxel distance to vary. Each point on a contour has a unique corresponding point in the nest frame. Experimental results showed that the proposed method computes accurate optical flow vectors for various moving contours.