Mismatched points are inevitable when matching the feature points in target recognition and image registration. The proper elimination of mismatched points improves the accuracy of recognition and registration, therefore, has become a focus of this research field. The currently mature elimination algorithms, such as random sample consensus (RANSAC) and M-estimator sample consensus (MSAC), often eliminate some of the correctly matched points. To overcome this shortcoming, this study proposes a mismatched-point elimination algorithm with double constraints on length and included angle based on the Pearson correlation coefficient. First, the mismatched points with larger error are roughly eliminated, and the mismatched points with smaller error are then precisely eliminated by iteration. In comparative experiments on several images, the proposed algorithm retained most of the correctly matched points while eliminating all of the wrong matched points. This performance was not matched by the comparative algorithms RANSAC and MSAC. Therefore, the proposed algorithm greatly reduces the error elimination rate and can significantly improve the accuracy of image matching.