A feature-based approach for dense segmentation and estimation of large disparity motion
Research output: Contribution to journal › Journal article › Research › peer-review
We present a novel framework for motion segmentation that combines the concepts of layer-based methods and feature-based 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 layer-based motion segmentation methods. We also present an extension that accounts for the case of non-planar motion, in which we use our planar motion segmentation results as an initialization for a regularized Thin Plate Spline fit. In addition, we present applications of our method to automatic object removal and to structure from motion.
Original language | English |
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Journal | International Journal of Computer Vision |
Volume | 68 |
Issue number | 2 |
Pages (from-to) | 125-143 |
Number of pages | 19 |
ISSN | 0920-5691 |
DOIs | |
Publication status | Published - Jun 2006 |
Externally published | Yes |
Bibliographical note
Funding Information:
We would like to thank Charless Fowlkes and Ben Ochoa for helpful discussions. The images in Figs. 13 and 14 are used courtesy of Dr. Philip Torr. This work was partially supported under the auspices of the U.S. Department of Energy by the Lawrence Livermore National Laboratory under contract No. W-7405-ENG-48, by an NSF IGERT Grant (Vision and Learning in Humans and Machines, #DGE-0333451), by an NSF CAREER Grant (Algorithms for Nonrigid Structure from Motion, #0448615) and by The Alfred P. Sloan Research Fellowship.
- Graph cuts, Layer-based motion, Markov Random Field, Metric labeling problem, Motion segmentation, Periodic motion, RANSAC
Research areas
ID: 302054149