Rigid and Articulated Point Registration with Expectation Conditional Maximization - Archive ouverte HAL Access content directly
Reports (Research Report) Year : 2009

Rigid and Articulated Point Registration with Expectation Conditional Maximization

(1) , (2) , (3) , (4) , (1)
1
2
3
4

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 analyse in detail the associated consequences in terms of estimation of the registration parameters, and we propose an optimal method for estimating the rotational and translational parameters based on semi-definite 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 we compare it both theoretically and experimentally with other robust methods for point registration.
Fichier principal
Vignette du fichier
RR-7114.pdf (11.78 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

inria-00435772 , version 1 (25-11-2009)
inria-00435772 , version 2 (25-11-2009)

Identifiers

  • HAL Id : inria-00435772 , version 2

Cite

Radu Horaud, Florence Forbes, Manuel Yguel, Guillaume Dewaele, Jian Zhang. Rigid and Articulated Point Registration with Expectation Conditional Maximization. [Research Report] RR-7114, INRIA. 2009. ⟨inria-00435772v2⟩
359 View
445 Download

Share

Gmail Facebook Twitter LinkedIn More