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Optimal Filters For Gradient-Based Motion Estimation
Elad, Michael; Teo, Patrick; Hel-Or, Yacov
Keyword(s): motion estimation; optical flow; pre-smoothing; gradients computation; optimal filters; constrained minimization
Abstract: Gradient based approaches for motion estimation (Optical-Flow) refer to those techniques that estimate the motion of an image sequence based on local changes in the image intensities. In order to best evaluate local changes in the intensities specific filters are applied to the image sequence. These filters are typically composed of a spatio-temporal pre-smoothing filter followed by derivative filters. The design of these filters plays an important role in the estimation accuracy. This paper proposes a method for the design of these filters in an optimal manner. Unlike previous approaches that design optimal derivative filters in some sense, the proposed technique defines the optimality directly with respect to the motion estimation goal. One possible result of the suggested approach is a set of image dependent filters, which can be computed prior to the estimation process. An alternative is generic filters, capable of treating the typical (natural) images. Simulations demonstrate the validity of the new design approach. Notes: Teo is from the Computer Science Department at Stanford University.
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