Issue No. 03 - March (2011 vol. 33)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.94
Radu Horaud , INRIA Grenoble Rhône-Alpes, Montbonnot Saint-Martin
Florence Forbes , INRIA Grenoble Rhône-Alpes, Montbonnot Saint-Martin
Manuel Yguel , INRIA Grenoble Rhône-Alpes, Montbonnot Saint-Martin
Guillaume Dewaele , INRIA Grenoble Rhône-Alpes, Montbonnot Saint-Martin
Jian Zhang , The University of Hong Kong, Hong Kong
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 in-depth analysis of our method and compare it both theoretically and experimentally with other robust methods for point registration.
Point registration, feature matching, articulated object tracking, hand tracking, object pose, robust statistics, outlier detection, expectation maximization, EM, ICP, Gaussian mixture models, convex optimization, SDP relaxation.
R. Horaud, M. Yguel, G. Dewaele, F. Forbes and J. Zhang, "Rigid and Articulated Point Registration with Expectation Conditional Maximization," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 587-602, 2010.