Optimal method for the affine F-matrix and its uncertainty estimation in the sense of both noise and outliers

We propose, in maximum likelihood sense, an optimal method for the affine fundamental matrix estimation in the presence of both Gaussian noise and outliers. It is based on weighting the squared residuals by the iteratively completed, residual posterior probabilities to be relevant. The proposed principle is also used for the covariance matrix estimation of the affine F-matrix where the novelty is in the fact that all data is used rather than the (erroneously) relevant classified matching points. The experiments on both synthetic and real data verify the optimality of the method in the sense of both false matches and Gaussian noise in data