Clustering Algorithm for the Fundamental Matrix Estimation

In the paper, Gaussian mixture model is used to describe the residuals of matches in the new robust algorithm for fundamental matrix estimation, and an improved split-merge EM (SMEM) algorithm is used to classify the matches, so that the false matches can be detected and rejected by the least mean absolute residual criteria. Finally, M-estimator is used to estimate the fundamental matrix. Our algorithm gives better result than random sample consensus (RANSAC) algorithm with higher efficiency in the large number experiments tested.