Rigid and Articulated Point Registration with Expectation Conditional Maximization

—This paper addresses the issue of matching rigid and articulated shapes through probabilistic point registration. The problem is recast into a missing data framework where unknown correspondences are handled via mixture models. Adopting a maximum likelihood principle, we introduce an innovative EM-like algorithm, namely, the Expectation Conditional Maximization for Point Registration (ECMPR) algorithm. The algorithm allows the use of general covariance matrices for the mixture model components and improves over the isotropic covariance case. We analyze in detail the associated consequences in terms of estimation of the registration parameters, and propose an optimal method for estimating the rotational and translational parameters based on semidefinite positive relaxation. We extend rigid registration to articulated registration. Robustness is ensured by detecting and rejecting outliers through the addition of a uniform component to the Gaussian mixture model at hand. We provide an ...

• Type: Journal
• Year: 2011
• Where: PAMI
• Authors: Radu Horaud, Florence Forbes, Manuel Yguel, Guillaume Dewaele, Jian Zhang
• Keywords: Mixture Model, Operations Research, PAMI 2011, Point Registration, Probabilistic Point Registration