You are not logged in.
Openly accessible

Pseudoconvex proximal splitting for L∞problems in multiview geometry

Eriksson,A and Isaksson,M 2014, Pseudoconvex proximal splitting for L∞problems in multiview geometry, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Washington, D.C., United States, pp. 4066-4073, doi: 10.1109/CVPR.2014.518.

Attached Files
Name Description MIMEType Size Downloads
isaksson-pseudoconvex-post-2014.pdf Authors' post print application/pdf 12.25MB 42

Title Pseudoconvex proximal splitting for L∞problems in multiview geometry
Author(s) Eriksson,A
Isaksson,M
Conference name Computer Vision and Pattern Recognition. Conference (2014 : Columbus, Ohio)
Conference location Columbus, Ohio
Conference dates 23-28 June 2014
Title of proceedings Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Editor(s) [Unknown]
Publication date 2014
Conference series IEEE Conference on Computer Vision and Pattern Recognition
Start page 4066
End page 4073
Total pages 8
Publisher IEEE Computer Society
Place of publication Washington, D.C., United States
Summary 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.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 9781479951178
ISSN 1063-6919
Language eng
DOI 10.1109/CVPR.2014.518
Field of Research 080104 Computer Vision
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2014, IEEE Computer Society
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30069053

Document type: Conference Paper
Collections: Centre for Intelligent Systems Research
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 4 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 73 Abstract Views, 51 File Downloads  -  Detailed Statistics
Created: Fri, 30 Jan 2015, 11:07:22 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.