CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 1997 vol.19 Issue No.05 - May
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.
Mads Nielsen, "Graduated Nonconvexity by Functional Focusing", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.19, no. 5, pp. 521-525, May 1997, doi:10.1109/34.589213