This paper describes a new method for matching, validating, and disambiguating features for stereo vision. It is based on the Marr-Poggio- Grimson stereo matching algorithm which uses zero-crossing contours in difference-of-Gaussian filtered images as features. The matched contours are represented in disparity space, which makes the information needed for matched contour validation and disambiguation easily accessible. The use of disparity space also makes the algorithm conceptually cleaner than previous implementations of the Marr-Poggio-Grimson algorithm and yields a more efficient matching process.
