A Novel Multi-Objective Optimization-Based Image Registration Method


The RANSAC is widely used in image registration algorithms. However, the RANSAC becomes computationally expensive when the number of feature points is large. And also, its high error matching ratio caused by the large number of iterations always raises the possibility of false registration. To deal with these drawbacks, a novel multi-objective optimization-based image registration method is proposed, named MO-IRM. In MO-IRM, a multi-objective estimation model is built to describe the feature matching pairs (data set), with no need for the pre-check process that is necessary in some improved RANSAC algorithms to eliminate the error-matching pairs. Moreover, a full variate Gaussian model-based RM-MEDA without clustering process (FRM-MEDA) is presented to solve the established multi-objective model. FRM-MEDA only requires a few iterations to find out a correct model. FRM-MEDA can not only greatly reduce the computational overhead but also effectively decrease the possibility of false registration. The proposed MO-IRM is compared with RM-MEDA, NSGA-square and the RANSAC based registration algorithm on the Dazu grottoes image database. The experiment results demonstrate that the proposed method achieves ideal registration performances on both two images and multiple images, and greatly outperforms the compared algorithms on the runtime.