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Graduated Nonconvexity by Functional Focusing
May 1997 (vol. 19 no. 5)
pp. 521-525

Abstract—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.

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Index Terms:
Graduated nonconvexity, functional minimization, mean field annealing, Bayesian reconstruction.
Citation:
Mads Nielsen, "Graduated Nonconvexity by Functional Focusing," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 5, pp. 521-525, May 1997, doi:10.1109/34.589213
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