Ensemble Registration of Multisensor Images by a Variational Bayesian Approach

Abstract

In this paper, a novel image registration method, called ensemble image registration, is proposed. We use an infinite Gaussian mixture model (IGMM), which is based on a joint Gaussian mixture model and a Dirichlet process (DP), to model the joint intensity scatter plot (JISP) of the unregistered images. The DP is the cornerstone of nonparametric Bayesian statistics, and has capability of determining a proper number of mixing components. To simultaneously register a group of images, the cost function of reducing the dispersion in the JISP is preformed by a Bayesian method using IGMM. A variational Bayesian framework is developed to inference the posterior distribution of the parameters in the IGMM. To evaluate the performance of the proposed method, experiments of ensemble image registration are presented. The results show that the proposed method has improved performance compared with conventional methods.