An extended Kalman filtering approach to high precision stereo image matching

We present a novel approach to stereo image matching for high precision applications which is based upon a non-linear filtering technique called the extended Kalman filter (EKF). The matching algorithm has three components - the matching process, false match rejection, and disparity prediction, which are all derived within the Kalman filtering framework. We first present the stereo matching model used, and then derive the matching equations and processes. We then present results of tests performed on a synthetic stereo pair, which allows comparison with ground truth data. The results indicate that the method is capable of very robust and high precision matching performance.