An Estimation Method Robust to Outliers for Multiple Homographies

For the multiple homographies estimation problem in the case of outliers, an initialization method of the multiple homographies estimation robust to outliers is proposed. In this method, the outlier rejection is integrated into the multiple homographies estimation based on the algebraic error and the structure similarity constraint of the key-point correspondences. As a result, the outliers can be removed effectively and the initialization value of multiple homographies can be estimated with a negligible computational overhead. Combining the AML-COV (approximate maximum likelihood with homography covariance) algorithm, several experiments based on simulation data and real images demonstrate the performance of the proposed method in subjective visual quality and objective measurement quality. The experimental results show that the proposed method can achieve accurate, efficient, and robust multiple homographies estimation and performs a good solution to the multiple homographies estimation problem in the case of outliers.