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  • 标题:Robust Optical Flow Estimation
  • 本地全文:下载
  • 作者:Javier Sánchez Pérez ; Nelson Monzón López ; Agustín Salgado de la Nuez
  • 期刊名称:Image Processing On Line
  • 电子版ISSN:2105-1232
  • 出版年度:2013
  • 卷号:3
  • 页码:242-260
  • DOI:10.5201/ipol.2013.21
  • 出版社:Image Processing On Line
  • 摘要:

    In this work, we describe an implementation of the variational method proposed by Brox et al. in 2004, which yields accurate optical flows with low running times. It has several benefits with respect to the method of Horn and Schunck: it is more robust to the presence of outliers, produces piecewise-smooth flow fields and can cope with constant brightness changes. This method relies on the brightness and gradient constancy assumptions, using the information of the image intensities and the image gradients to find correspondences. It also generalizes the use of continuous L1 functionals, which help mitigate the effect of outliers and create a Total Variation (TV) regularization. Additionally, it introduces a simple temporal regularization scheme that enforces a continuous temporal coherence of the flow fields.

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