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Sparse Aggregation Framework for Optical Flow Estimation

  • Denis FortunEmail author
  • Patrick Bouthemy
  • Charles Kervrann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9087)

Abstract

We propose a sparse aggregation framework for optical flow estimation to overcome the limitations of variational methods introduced by coarse-to-fine strategies. The idea is to compute parametric motion candidates estimated in overlapping square windows of variable size taken in the semi-local neighborhood of a given point. In the second step, a sparse representation and an optimization procedure in the continuous setting are proposed to compute a motion vector close to motion candidates for each pixel. We demonstrate the feasibility and performance of our two-step approach on image pairs and compare its performances with competitive methods on the Middlebury benchmark.

Keywords

Motion estimation Optical flow Sparse representation Optimization 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Denis Fortun
    • 1
    Email author
  • Patrick Bouthemy
    • 1
  • Charles Kervrann
    • 1
  1. 1.Centre de Rennes - Bretagne AtlantiqueInriaRennesFrance

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