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• W3005358710 abstract "We propose a variant of convex reformulation of the standard variational model with non-convex data terms. The proposed convex relaxation of multilabel problems is a continuous formulation of Ishikawa’s method for extension of the graph min-cut to the multilabel problems. Our convex continuous reformulation is based upon functional lifting to a higher-dimensional space using superlevel functions. We solve the resulting convex variational problem by the augmented Lagrangian method. The most time consuming part of this method is the numerical solution of a boundary value problem for the Poisson equation in three-dimensional space, which is implemented by means of a fast Poisson solver. We illustrate the developed theory with several numerical examples for the standard correspondence problem for a rectified stereo image pair." @default.
• W3005358710 created "2020-02-14" @default.
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• W3005358710 date "2019-08-01" @default.
• W3005358710 modified "2023-09-25" @default.
• W3005358710 title "Variational Model for Depth Estimation from Images" @default.
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• W3005358710 doi "https://doi.org/10.1109/skima47702.2019.8982425" @default.
• W3005358710 hasPublicationYear "2019" @default.
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