Abstract
This paper presents an extension to the maximum likelihood estimation sample consensus (MLESAC) algorithm by estimating the prior validity of correspondences using both the measured data and a model hypothesis. Validity is determined based on the data set associated with the model that is considered as the best one so far in the previous random trials. The proposed robust algorithm is applied to estimate the fundamental matrix using randomly generated synthetic test data. Experiment results show that at various outlier ratios the proposed algorithm reduces the Sampson error and is also faster (in terms of the number of trials) in comparison to other conventional algorithms.