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Parallel and Distributed Vision Algorithms Using Dual Decomposition

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Author

  • Petter Strandmark
  • Fredrik Kahl
  • Thomas Schoenemann

Summary, in English

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.

Publishing year

2011

Language

English

Pages

1721-1732

Publication/Series

Computer Vision and Image Understanding

Volume

115

Issue

12

Document type

Journal article

Publisher

Elsevier

Topic

  • Mathematics
  • Computer Vision and Robotics (Autonomous Systems)

Keywords

  • Graph cuts
  • Dual decomposition
  • Parallel
  • MRF
  • MPI
  • GPU

Status

Published

Research group

  • Mathematical Imaging Group

ISBN/ISSN/Other

  • ISSN: 1077-3142