@InProceedings{MohammadRouhani2011,
author="Mohammad Rouhani
and Angel Sappa",
title="Correspondence Free Registration through a Point-to-Model Distance Minimization",
booktitle="13th IEEE International Conference on Computer Vision",
year="2011",
pages="2150--2157",
abstract="This paper presents a novel formulation, which derives in a smooth minimization problem, to tackle the rigid registration between a given point set and a model set. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, we propose to describe the model set by means of an implicit representation. It allows a new definition of the registration error, which works beyond the point level representation. Moreover, it could be used in a gradient-based optimization framework. The proposed approach consists of two stages. Firstly, a novel formulation is proposed that relates the registration parameters with the distance between the model and data set. Secondly, the registration parameters are obtained by means of the Levengberg-Marquardt algorithm. Experimental results and comparisons with state of the art show the validity of the proposed framework.",
optnote="ADAS",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1832), last updated on Tue, 11 Mar 2014 15:26:24 +0100",
isbn="978-1-4577-1101-5",
issn="1550-5499",
doi="10.1109/ICCV.2011.6126491"
}