Total variation for cyclic structures: Convex relaxation and efficient minimization

Evgeny Strekalovskiy, Daniel Cremers

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

35 Scopus citations

Abstract

We introduce a novel type of total variation regularizer, TV S1 , for cyclic structures such as angles or hue values. The method handles the periodicity of values in a simple and consistent way and is invariant to value shifts. The regularizer is integrated in a recent functional lifting framework which allows for arbitrary nonconvex data terms. Results are superior and more natural than with the simple total variation without special care about wrapping interval end points. In addition we propose an equivalent formulation which can be minimized with the same time and memory efficiency as the standard total variation.

Original languageEnglish
Title of host publication2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
PublisherIEEE Computer Society
Pages1905-1911
Number of pages7
ISBN (Print)9781457703942
DOIs
StatePublished - 2011

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

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