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What and Who

Sublabel Accurate Relaxation of Nonconvex Energies arising in Computer Vision Problems

Emanuel Laude
TU Munich
Talk
AG 2, AG 4, MMCI  
AG Audience
English

Date, Time and Location

Tuesday, 12 July 2016
14:00
45 Minutes
E1 4
633
Saarbrücken

Abstract

We propose a novel spatially continuous framework for convex relaxations based on functional lifting. Our method can be interpreted as a sublabel-accurate solution to multilabel problems. We show that previously proposed functional lifting methods optimize an energy which is linear between two labels and hence require (often infinitely) many labels for a faithful approximation. In contrast, the proposed formulation is based on a piecewise convex approximation and therefore needs far fewer labels. In comparison to recent MRF-based approaches, our method is formulated in a spatially continuous setting and shows less grid bias. Moreover, in a local sense, our formulation is the tightest possible convex relaxation. It is easy to implement and allows an efficient primal-dual optimization on GPUs. We show the effectiveness of our approach on several computer vision problems.

Contact

Björn Andres
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Tags, Category, Keywords and additional notes

convex optimization; computer vision

Björn Andres, 06/09/2016 10:49 -- Created document.