A Bayesian weighting principle for the fundamental matrix estimation ?
We propose a new approach to the fundamental matrix estimation problem in stereo vision, which is based on the optimal weighting of the matching points by their a posteriori probability to be relevant. With the affine camera model the method produces better results compared to the known robust methods including those with the full camera model. The same approach with the full perspective projection case is much more difficult to solve, however, the results obtained with the proposed method are promising.
fundamental matrix stereo vision outlier rejection epipolar geometry correspondence problem