A new method is presented for robustly estimating the fundamental matrix from image correspondence. Starting from RANSAC and MLESAC algorithm

we addressed some problems posed from both practical and theoretical standpoints and propose a new algorithm GMSAC. GMSAC adopts the same sampling strategy and maximization likelihood theory as the previous approaches. But instead of uniform distribution used by MLESAC

GMSAC choose Gaussian mixture to model the outliers. Due to the complex nature of outliers’ occurrence

Gaussian mixture is more suitable for the distribution of outliers. We make a detailed analysis on the formation of outliers

and model different types of outliers respectively. Results are given for several image sequences

and it is demonstrated that this method gives results superior to the original MLESAC.