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Pseudoconvex proximal splitting for L∞problems in multiview geometry

Version 2 2024-06-06, 11:44
Version 1 2015-01-15, 14:41
conference contribution
posted on 2024-06-06, 11:44 authored by A Eriksson, M Isaksson
In this paper we study optimization methods for minimizing large-scale pseudoconvex L∞problems in multiview geometry. We present a novel algorithm for solving this class of problem based on proximal splitting methods. We provide a brief derivation of the proposed method along with a general convergence analysis. The resulting meta-algorithm requires very little effort in terms of implementation and instead makes use of existing advanced solvers for non-linear optimization. Preliminary experiments on a number of real image datasets indicate that the proposed method experimentally matches or outperforms current state-of-the-art solvers for this class of problems.

History

Pagination

4066-4073

Location

Columbus; United States

Start date

2014-06-23

End date

2014-06-28

ISSN

1063-6919

ISBN-13

9781479951178

Language

eng

Notes

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Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2014, IEEE Computer Society

Editor/Contributor(s)

[Unknown]

Title of proceedings

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Event

Computer Vision and Pattern Recognition. Conference (2014 : Columbus, Ohio)

Publisher

IEEE Computer Society

Place of publication

Washington, D.C., United States

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