@InProceedings{MohammadRouhani2012,
author="Mohammad Rouhani
and Angel Sappa",
title="Non-Rigid Shape Registration: A Single Linear Least Squares Framework",
booktitle="12th European Conference on Computer Vision",
year="2012",
publisher="Springer Berlin Heidelberg",
volume="7578",
pages="264--277",
abstract="This paper proposes a non-rigid registration formulation capturing both global and local deformations in a single framework. This formulation is based on a quadratic estimation of the registration distance together with a quadratic regularization term. Hence, the optimal transformation parameters are easily obtained by solving a liner system of equations, which guarantee a fast convergence. Experimental results with challenging 2D and 3D shapes are presented to show the validity of the proposed framework. Furthermore, comparisons with the most relevant approaches are provided.",
optnote="ADAS",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2158), last updated on Thu, 13 Mar 2014 11:07:35 +0100",
isbn="978-3-642-33785-7",
issn="0302-9743",
doi="10.1007/978-3-642-33786-4_20"
}