Layered image motion with explicit occlusions, temporal consistency, and depth ordering

author: Deqing Sun, Computer Science Department, Brown University
published: March 25, 2011,   recorded: December 2010,   views: 213
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Description

Layered models are a powerful way of describing natural scenes containing smooth surfaces that may overlap and occlude each other. For image motion estimation, such models have a long history but have not achieved the wide use or accuracy of non-layered methods. We present a new probabilistic model of optical flow in layers that addresses many of the shortcomings of previous approaches. In particular, we define a probabilistic graphical model that explicitly captures:

  1. occlusions and disocclusions;
  2. depth ordering of the layers;
  3. temporal consistency of the layer segmentation. Additionally the optical flow in each layer is modeled by a combination of a parametric model and a smooth deviation based on an MRF with a robust spatial prior; the resulting model allows roughness in layers. Finally, a key contribution is the formulation of the layers using an image-dependent hidden field prior based on recent models for static scene segmentation. The method achieves state-of-the-art results on the Middlebury benchmark and produces meaningful scene segmentations as well as detected occlusion regions.

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Download slides icon Download slides: nips2010_sun_lim_01.pdf (3.6 MB)

Download article icon Download article: nips2010_0266.pdf (1.6 MB)


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