Optical flow estimation with uncertainties through dynamic MRFs

Abstract : In this paper, we propose a novel dynamic discrete framework to address image morphing with application to optical flow estimation. We reformulate the problem using a number of discrete displacements, and therefore the estimation of the morphing parameters becomes a tractable matching criteria independent combinatorial problem which is solved through the FastPD algorithm. In order to overcome the main limitation of discrete approaches (low dimensionality of the label space is unable to capture the continuous nature of the expected solution), we introduce a dynamic behavior in the model where the plausible discrete deformations (displacements) are varying in space (across the domain) and time (different states of the process- successive morphing states) according to the local uncertainty of the obtained solution. 1.
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Contributor : Enzo Ferrante <>
Submitted on : Saturday, December 14, 2013 - 11:58:58 AM
Last modification on : Tuesday, February 5, 2019 - 1:52:14 PM

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Glocker Ben, Nikos Paragios, Nikos Komodakis, Georgios Tziritas, Nassir Navab. Optical flow estimation with uncertainties through dynamic MRFs. Optical flow estimation with uncertainties through dynamic MRFs, 2008, United States. pp.1-8. ⟨hal-00918711⟩

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