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A Highly Efficient GPU Implementation for Variational Optic Flow Based on the Euler-Lagrange Framework

  • Pascal Gwosdek
  • Henning Zimmer
  • Sven Grewenig
  • Andrés Bruhn
  • Joachim Weickert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6554)

Abstract

The Euler-Lagrange (EL) framework is the most widely-used strategy for solving variational optic flow methods. We present the first approach that solves the EL equations of state-of-the-art methods on sequences with \(640 \!\times\! 480\) pixels in near-realtime on GPUs. This performance is achieved by combining two ideas: (i) We extend the recently proposed Fast Explicit Diffusion (FED) scheme to optic flow, and additionally embed it into a coarse-to-fine strategy. (ii) We parallelise our complete algorithm on a GPU, where a careful optimisation of global memory operations and an efficient use of on-chip memory guarantee a good performance. Applying our approach to the variational ‘Complementary Optic Flow’ method (Zimmer et al. (2009)), we obtain highly accurate flow fields in less than a second. This currently constitutes the fastest method in the top 10 of the widely used Middlebury benchmark.

Keywords

Global Memory Data Term Smoothness Term British Machine Vision Constancy Assumption 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pascal Gwosdek
    • 1
  • Henning Zimmer
    • 1
  • Sven Grewenig
    • 1
  • Andrés Bruhn
    • 2
  • Joachim Weickert
    • 1
  1. 1.Mathematical Image Analysis Group, Faculty of Mathematics and Computer ScienceSaarland UniversitySaarbrückenGermany
  2. 2.Vision and Image Processing Group,Cluster of Excellence Multimodal Computing and InteractionSaarland UniversitySaarbrückenGermany

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