Rigid and Articulated Point Registration with Expectation Conditional Maximization

Authors:
Radu Horaud;Florence Forbes;Manuel Yguel;Guillaume Dewaele;Jian Zhang
Affiliations:
INRIA Grenoble Rhône-Alpes, Montbonnot Saint-Martin;INRIA Grenoble Rhône-Alpes, Montbonnot Saint-Martin;INRIA Grenoble Rhône-Alpes, Montbonnot Saint-Martin;INRIA Grenoble Rhône-Alpes, Montbonnot Saint-Martin;The University of Hong Kong, Hong Kong
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2011

Citing 0
Cited 8

Probabilistic deformable surface tracking from multiple videos

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Learning shape segmentation using constrained spectral clustering and probabilistic label transfer

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Smooth point-set registration using neighboring constraints

Pattern Recognition Letters
SHREC'10 track: correspondence finding

EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
Robust non-rigid point registration based on feature-dependant finite mixture model

Pattern Recognition Letters
Elastic image registration using hierarchical spatially based mean shift

Computers in Biology and Medicine
EM-GPA: Generalized Procrustes analysis with hidden variables for 3D shape modeling

Computer Vision and Image Understanding
Bayesian perspective for the registration of multiple 3D views

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.14

Visualization

Abstract

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.