%0 Conference Proceedings
%T Correspondence Free Registration through a Point-to-Model Distance Minimization
%A Mohammad Rouhani
%A Angel Sappa
%B 13th IEEE International Conference on Computer Vision
%D 2011
%@ 1550-5499
%@ 978-1-4577-1101-5
%F Mohammad Rouhani2011
%O ADAS
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=1832), last updated on Tue, 11 Mar 2014 15:26:24 +0100
%X 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.
%U http://dx.doi.org/10.1109/ICCV.2011.6126491
%P 2150-2157