A robust non-iterative method for similarity transform estimation

A non-iterative and robust method - direct outliers remove (DOR) is proposed, which efficiently estimates the similarity transform based on a data set containing both correct and incorrect correspondences. Unlike hypothesize-and-test methods such as Random Sample Consensus algorithm and its variants, DOR removes mismatches by exploring all the correspondences only once, using two invariant features of similarity transform. One is the angles between two vectors and the other is the length ratios of corresponding vectors. Given two images related by similarity transform, experiments demonstrate that all the mismatches introduced in matching stage could be detected and removed. Without losing computational accuracy, DOR is faster compared with several hypothesize-and-test algorithms, especially when the percentage of correct correspondence is relatively low.