Smoothing of optical flow using robustified diffusion kernels
Date
2010
Authors
Doshi, A.
Bors, A.G.
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Journal Title
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Type:
Journal article
Citation
Image and Vision Computing, 2010; 28(12):1575-1589
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Abstract
This paper proposes a new optical flow smoothing methodology combining vector diffusion and robuststatistics. Vector smoothing using diffusion preserves moving object boundaries and the main motiondiscontinuities. According to a study provided in the paper, diffusion does not remove the outliers butspreads them out, introducing a bias in the neighbourhood. In this paper robust statistics operators such asthe median and alpha-trimmed mean are considered for robustifying the diffusion kernels. The robustdiffusion smoothing process is extended to 3-D lattices as well. The proposed algorithms are applied forsmoothing artificially generated vector fields as well as the optical flow estimated from image sequences.
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Dissertation Note
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Data source: Figures & tables, https://doi.org/10.1016/j.imavis.2010.04.001
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Copyright 2010 Elsevier BV