A Robust Technique for Matching Two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry

Abstract

This paper proposes a robust approach to image matching by exploiting the only available geometric constraint, namely, the epipolar constraint. The images are uncalibrated, namely the motion between them and the camera parameters are not known. Thus, the images can be taken by different cameras or a single camera a different time instance. If we make an exhaustive search for the epipolar geometry, the complexity is prohibitively high. The idea underlying our approach is to use some classical techniques ( correlation and relaxation methods in our particular implementation) to find an initial set of matches, and then use a robust technique – the Least Median of Squares (LMedS) – to discard false adapted criterion. More matches are eventually found, as in stereo matching, by using the recovered epipolar geometry. A large number of experiments have been carried out, and very good results have been obtained. 

Regarding the relaxation technique, we define a new measure of matching support, which allows a higher tolerance to deformation with respect to rigid transformations in the image plane and a smaller contribution for distant matches than for nearby ones. A new strategy for updating matches is developed, which only selects those batches having both high matching support and low matching ambiguity. The update strategy is different from the classical winner take all which is easily stuck at a local minimum, and also from loser take nothing, which is usually very slow. The proposed algorithm has been widely tested and works remarkably well in a scene with many repetitive patterns.