Issue No. 05 - May (1997 vol. 19)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.589213
<p><b>Abstract</b>—Reconstruction of noise-corrupted surfaces may be stated as a (in general nonconvex) functional minimization problem. For functionals with quadratic data term, this paper addresses the criteria for such functionals to be convex, and the variational approach for minimization. I present two automatic and general methods of approximation with convex functionals based on Gaussian convolution. They are compared to the Blake-Zisserman graduated nonconvexity (GNC) method and Bilbro et al. and Geiger and Girosi's mean field annealing (MFA) of a weak membrane.</p>
Graduated nonconvexity, functional minimization, mean field annealing, Bayesian reconstruction.
M. Nielsen, "Graduated Nonconvexity by Functional Focusing," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 19, no. , pp. 521-525, 1997.