Recursive Regularization Filters: Design, Properties, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel and Deterministic Algorithms from MRFs: Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Binocular dense depth reconstruction using isotropy constraint
Selected papers from the 9th Scandinavian conference on Image analysis : theory and applications of image analysis II: theory and applications of image analysis II
Regularization, Scale-Space, and Edge Detection Filters
Journal of Mathematical Imaging and Vision
A Level-Set Approach to 3D Reconstruction from Range Data
International Journal of Computer Vision
SOM and neural gas as graduated nonconvexity algorithms
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
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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.