Robust Point Set Registration Based on Bayesian Student's t Mixture Model

For the interference problem of outliers on the point set registration, a robust affine point set registration method based on Bayesian student's t mixture model (SMM) is proposed. Under Bayesian framework, the point set registration problem is formularized as the probability density estimation problem by using the SMM. By introducing the approximate variational posterior distribution, the objective function is converted to maximize the variational lower bound of complete data log-likelihood, and the variational Bayesian expectation maximization (VBEM) method is used to estimate the variational posterior distribution of model parameters iteratively. The free degree of student t distribution is estimated by maximizing the complete data log-likelihood, and it is approximated by using the Stirling formula. Registration experiments on simulated point sets and optical remote sensing images verify the effectiveness and feasibility of the proposed method.