Scene Flow Estimation using Intelligent Cost Functions (bibtex)
by Simon Hadfield, Richard Bowden
Abstract:
Motion estimation algorithms are typically based upon the assumption of brightness constancy or related assumptions such as gradient constancy. This manuscript evaluates several common cost functions from the motion estimation literature, which embody these assumptions. We demonstrate that such assumptions break for real world data, and the functions are therefore unsuitable. We propose a simple solution, which significantly increases the discriminatory ability of the metric, by learning a nonlinear relationship using techniques from machine learning. Furthermore, we demonstrate how context and a nonlinear combination of metrics, can provide additional gains, and demonstrating a 44% improvement in the performance of a state of the art scene flow estimation technique. In addition, smaller gains of 20% are demonstrated in optical flow estimation tasks.
Reference:
Scene Flow Estimation using Intelligent Cost Functions (Simon Hadfield, Richard Bowden), In Proceedings of the British Conference on Machine Vision (BMVC) (Michel Valstar, Andrew French, Tony Pridmore, eds.), BMVA Press, 2014. (Extended abstract, Poster, Supplementary results)
Bibtex Entry:
@InProceedings{Hadfield14c,
  Title                    = {Scene Flow Estimation using Intelligent Cost Functions},
  Author                   = {Simon Hadfield and Richard Bowden},
  Booktitle                = {Proceedings of the British Conference on Machine Vision (BMVC)},
  Year                     = {2014},

  Address                  = {Nottingham, UK},
  Editor                   = {Valstar, Michel and French, Andrew and Pridmore, Tony},
  Month                    = {1 -- 5 } # sep,
  Publisher                = {BMVA Press},

  Abstract                 = {Motion estimation algorithms are typically based upon the assumption of brightness constancy or related assumptions such as gradient constancy. This manuscript evaluates several common cost functions from the motion estimation literature, which embody these assumptions. We demonstrate that such assumptions break for real world data, and the functions are therefore unsuitable. We propose a simple solution, which significantly increases the discriminatory ability of the metric, by learning a nonlinear relationship using techniques from machine learning. Furthermore, we demonstrate how context and a nonlinear combination of metrics, can provide additional gains, and demonstrating a 44% improvement in the performance of a state of the art scene flow estimation technique. In addition, smaller gains of 20% are demonstrated in optical flow estimation tasks.},
  Comment                  = {<a href="http://personal.ee.surrey.ac.uk/Personal/S.Hadfield/papers/Scene%20Flow%20Estimation%20using%20Intelligent%20Cost%20Functions%20-%20Extended%20abstract.pdf">Extended abstract</a>, <a href="http://personal.ee.surrey.ac.uk/Personal/S.Hadfield/posters/Scene%20Flow%20Estimation%20using%20Intelligent.tif">Poster</a>, <a href="http://personal.ee.surrey.ac.uk/Personal/S.Hadfield/icf.html">Supplementary results</a>},
  Crossref                 = {BMVC14},
  Doi                      = {10.5244/C.28.108},
  Gsid                     = {12162510449030781704},
  Keywords                 = {intelligent cost functions, scene flow, error metric, optical flow, motion estimation, machine learning, gaussian process},
  Timestamp                = {2014.07.30},
  Url                      = {http://personal.ee.surrey.ac.uk/Personal/S.Hadfield/papers/Scene%20Flow%20Estimation%20using%20Intelligent%20Cost%20Functions.pdf}
}
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