Robust Line Matching and Estimate of Homographies Simultaneously

This paper addresses the robust matching of lines simultaneously to the computation of homographies between two views, when structure and motion are unknown. Using viewpoint non invariant measures, such as image dependent parameters, gives a lot of non matched or wrong matched features. The inclusion of projective transformations gives much better results with short computing overload. We use line features which can usually be extracted more accurately than points and they can be used in cases when there are partial occlusion. In the first stage, the lines are matched to the weighted nearest neighbor using brightness-based and geometric-based image parameters. From them, robust homographies can be computed, allowing to reject wrong matches, and growing also additional matches in the final stage. Although lines and points are dual features to compute homographies, some problems related to data representation and normalization using lines are considered. Results show that the robust technique turns out stable, and its application is useful in many situations. We have used it for robot homing and we also present automatic matching of lines at aerial images.