Estimation of Translation, Rotation and Large Scale Scaling Based on Multiple Scaling Assumptions

Abstract

We will be able to use highly parallel processing environments. This paper proposes a method for estimating translations, rotations and scaling reaching 10 times simultaneously based on the multiple scaling assumptions, and represents its performance with motion estimation experiments. A sector region luminosity correlation is used for estimating motion vectors. The sector region luminosity correlation is robust about the rotation and withstands large motion environments. The proposed method makes the assumptions about the scaling and estimates the motion vectors based on the assumptions. Then it randomly creates the pair of the estimated motion vectors. Next, it selects the proper pair using the pre-assumed scaling factor. The selected pairs are included in the set of reliable motion vector pairs. The reliable motion vector pairs decide the translation, rotation and scaling. With large scaling, it is difficult to estimate the motion using the sector region luminosity correlation. But with the assumptions about the scaling, they can work. Experiments show that the proposed method makes much better correlations between images than SIFT does in 10 times scaling changes.