%0
%0 Journal Article
%A Brunet, Florent; Bartoli, Adrien; Malgouyres, Rémy & Navab, Nassir
%D 2008
%T L-Tangent Norm: A Low Computational Cost Criterion for Choosing Regularization Weights and its Use for Range Surface Reconstruction
%E
%B Proceedings of 3D Data Processing, Visualization and Transmission
%C
%I
%V
%6
%N
%P
%&
%Y
%S
%7
%8
%9
%?
%!
%Z
%@
%(
%)
%*
%L
%M
%2
%3 article
%4
%#
%$
%F brunet2008ltn
%K
%X We are interested in fitting a surface model such as a tensor-product spline to range image data. This is commonly done by finding control points which minimize a compound cost including the goodness of fit and a regularizer, balanced by a regularization parameter. Many approaches choose this parameter as the minimizer of, for example, the cross-validation score or the L-curve criterion. Most of these criteria are expensive to compute and difficult to minimize.
We propose a novel criterion, the L-tangent norm, which overcomes these drawbacks. It gives sensible results with a much lower computational cost. This new criterion has been successfully tested with synthetic and real range image data.
%Z
%+
%^