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Optical flow refinement using iterative propagation under colour, proximity and flow reliability constraints

Abstract : This study proposes a strategy to refine optical flow based on the estimated reliability maps. These maps are firstly estimated a posteriori after the motion estimation by the well-known Kanade–Lucas–Tomasi (KLT). With two new defined criteria based, respectively, on the optical flow local variance and the temporal evolution of the KLT residuals, a global refinement of the motion map is then carried out through two stages under the control of the reliability measures and the colour local homogeneousness. According to the experiments performed on the Middlebury dataset, the authors' reliability measures prove to be a good indicator for the quality of the estimation. Indeed, the correction process increases the global reliability measures and reduces the global errors in a significant way. The experiments show that the quality is higher than classical estimation methods and ranked at 88/168 on Middlebury website
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https://hal.archives-ouvertes.fr/hal-02650221
Contributor : Frédéric Davesne <>
Submitted on : Friday, May 29, 2020 - 1:31:08 PM
Last modification on : Wednesday, September 16, 2020 - 5:20:19 PM

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Tan Khoa Mai, Michèle Gouiffès, Samia Bouchafa. Optical flow refinement using iterative propagation under colour, proximity and flow reliability constraints. IET Image Processing, Institution of Engineering and Technology, 2020, 14 (8), pp.1509--1519. ⟨10.1049/iet-ipr.2019.0370⟩. ⟨hal-02650221⟩

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