A Robust Greedy Algorithm for Estimating the Fundamental Matrix

By analyzing the shortcoming of existing robust algorithms based on random sampling employed in estimating the fundamental matrix,a novel algorithm is proposed for optimal estimation of fundamental matrix. The algorithm firstly uses some robust techniques to construct an inliers set of matching points.Then it takes the epipolar distance as the optimal criterion and search optimal subset in the inliers set under the greedy strategy.Finally,the fundamental matrix is calculated with the optimal subset. Experimental results on real image and synthetic data show that the proposed algorithm is superior over other robust methods in terms of estimation accuracy,ant-i noise ability and stability of epipoles.