Dynamic RANSAC

In this paper, the classical RANSAC approach is considered for robust matching to remove mismatches (outliers) in a list of putative correspondences. We will examine the justification for using the minimal size of sample set in a RANSAC trial and propose that the size of the sample set should be varied dynamically depending on the noise and data set. Using larger sample set will not increase the number of iterations dramatically but it can provide a more reliable solution. A new adjusting factor is added into the original RANSAC sampling equation such that the equation can model the noisy world better. In the proposed method, the noise variances, percentage of outliers and number of iterations are all estimated iteratively. Experimental results show that the estimated parameters are close to the ground truth. The modification can also be applied to any sampling consensus methods extended from RANSAC.