Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems

November 21, 2016 Β· Declared Dead Β· πŸ› IEEE International Conference on Computer Vision

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Authors Thomas MΓΆllenhoff, Daniel Cremers arXiv ID 1611.06987 Category cs.CV: Computer Vision Citations 14 Venue IEEE International Conference on Computer Vision Last Checked 4 months ago
Abstract
In this work we show how sublabel-accurate multilabeling approaches can be derived by approximating a classical label-continuous convex relaxation of nonconvex free-discontinuity problems. This insight allows to extend these sublabel-accurate approaches from total variation to general convex and nonconvex regularizations. Furthermore, it leads to a systematic approach to the discretization of continuous convex relaxations. We study the relationship to existing discretizations and to discrete-continuous MRFs. Finally, we apply the proposed approach to obtain a sublabel-accurate and convex solution to the vectorial Mumford-Shah functional and show in several experiments that it leads to more precise solutions using fewer labels.
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