Parallel and Distributed Vision Algorithms Using Dual Decomposition
(2011) In Computer Vision and Image Understanding 115(12). p.1721-1732- Abstract
- We investigate dual decomposition approaches for optimization problems arising in low-level vision. Dual decomposition can be used to parallelize existing algorithms, reduce memory requirements and to obtain approximate solutions of hard problems. An extensive set of experiments are performed for a variety of application problems including graph cut segmentation, curvature regularization and more generally the optimization of MRFs. We demonstrate that the technique can be useful for desktop computers, graphical processing units and supercomputer clusters. To facilitate further research, an implementation of the decomposition methods is made publicly available.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2060506
- author
- Strandmark, Petter LU ; Kahl, Fredrik LU and Schoenemann, Thomas LU
- organization
- publishing date
- 2011
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Graph cuts, Dual decomposition, Parallel, MRF, MPI, GPU
- in
- Computer Vision and Image Understanding
- volume
- 115
- issue
- 12
- pages
- 1721 - 1732
- publisher
- Elsevier
- external identifiers
-
- wos:000297085500011
- scopus:80455164637
- ISSN
- 1077-3142
- DOI
- 10.1016/j.cviu.2011.06.012
- language
- English
- LU publication?
- yes
- id
- bd4eda0b-f116-4a63-bafa-a6d12c4f3cde (old id 2060506)
- alternative location
- http://www.sciencedirect.com/science/article/pii/S1077314211001652
- date added to LUP
- 2016-04-01 11:15:35
- date last changed
- 2022-03-12 21:06:08
@article{bd4eda0b-f116-4a63-bafa-a6d12c4f3cde, abstract = {{We investigate dual decomposition approaches for optimization problems arising in low-level vision. Dual decomposition can be used to parallelize existing algorithms, reduce memory requirements and to obtain approximate solutions of hard problems. An extensive set of experiments are performed for a variety of application problems including graph cut segmentation, curvature regularization and more generally the optimization of MRFs. We demonstrate that the technique can be useful for desktop computers, graphical processing units and supercomputer clusters. To facilitate further research, an implementation of the decomposition methods is made publicly available.}}, author = {{Strandmark, Petter and Kahl, Fredrik and Schoenemann, Thomas}}, issn = {{1077-3142}}, keywords = {{Graph cuts; Dual decomposition; Parallel; MRF; MPI; GPU}}, language = {{eng}}, number = {{12}}, pages = {{1721--1732}}, publisher = {{Elsevier}}, series = {{Computer Vision and Image Understanding}}, title = {{Parallel and Distributed Vision Algorithms Using Dual Decomposition}}, url = {{http://dx.doi.org/10.1016/j.cviu.2011.06.012}}, doi = {{10.1016/j.cviu.2011.06.012}}, volume = {{115}}, year = {{2011}}, }