What went where

  • Authors:

  • Josh Wills;Sameer Agarwal;Serge Belongie

  • Affiliations:

  • Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA;Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA;Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA

  • Venue:
  • CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

We present a novel framework for motion segmentation that combines the concepts of layer-based methods and featurebased motion estimation. We estimate the initial correspondences by comparing vectors of filter outputs at interest points, from which we compute candidate scene relations via random sampling of minimal subsets of correspondences. We achieve a dense, piecewise smooth assignment of pixels to motion layers using a fast approximate graphcut algorithm based on a Markov random field formulation. We demonstrate our approach on image pairs containing large inter-frame motion and partial occlusion. The approach is efficient and it successfully segments scenes with inter-frame disparities previously beyond the scope of layerbased motion segmentation methods.