%0 Conference Paper
%D 1992
%T Constrained implicit function fitting
%A Gabriel Taubin
%A Bolle, R. M.
%A Vemuri, B. C.
%K 2D curves computer vision constrained implicit function fitting curve normal constraints eigenvalues and eigenfunctions function evaluation generalized eigenvalue problem pattern recognition point location shape description stability
%P 268-271
%X Describes techniques for stabilizing the implicit function fitting process. The key drawback of implicit function fitting methods described in literature thus far has been the stability with respect to outliners in the data. In this paper methods for stabilizing the implicit function fitting using additional constraints in the form of surface (curve) normals are described. These constraints eliminate the problem of sensitivity of the implicit function fitting method to outliners in the data. The authors demonstrate that in certain cases the fitting process can be reduced to a generalized eigenvalue problem that can be efficiently solved by standard numerical procedures. Preliminary experimental results with 2D curves consisting of point location and curve normal constraints as data are encouraging