CMU talk: Consistent Segmentation for Optical Flow Estimation

Larry Zitnick

When: Monday, November 21, 3:30 p.m.- 4:45 p.m.
Abstract:
The computation of optical flow in the presence of large displacements and occlusion boundaries is a difficult problem, both in terms of accuracy and computational efficiency. Recently, work in stereo vision has shown promising results using image segmentation to constrain the matching process. Unfortunately, these same segmentation approaches are highly inefficient for optical flow estimation, due to the increased search space (2D vs. 1D) needed for optical flow. We propose a new approach that simultaneously computes a consistent segmentation across images while estimating optical flow. This approach leads to a computationally efficient algorithm while producing accurate results. In addition, we'll present results in video interpolation and exaggerated motion blur using the computed flow fields.