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Article: Robust algebraic segmentation of mixed rigid-body and planar motions from two views
Title | Robust algebraic segmentation of mixed rigid-body and planar motions from two views |
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Authors | |
Keywords | Algebraic segmentation Epipolar geometry Homography Influence function Motion segmentation Outlier rejection |
Issue Date | 2010 |
Citation | International Journal of Computer Vision, 2010, v. 88, n. 3, p. 425-446 How to Cite? |
Abstract | This paper studies segmentation of multiple rigid-body motions in a 3-D dynamic scene under perspective camera projection. We consider dynamic scenes that contain both 3-D rigid-body structures and 2-D planar structures. Based on the well-known epipolar and homography constraints between two views, we propose a hybrid perspective constraint (HPC) to unify the representation of rigid-body and planar motions. Given a mixture of K hybrid perspective constraints, we propose an algebraic process to partition image correspondences to the individual 3-D motions, called Robust Algebraic Segmentation (RAS). Particularly, we prove that the joint distribution of image correspondences is uniquely determined by a set of (2K)-th degree polynomials, a global signature for the union of K motions of possibly mixed type. The first and second derivatives of these polynomials provide a means to recover the association of the individual image samples to their respective motions. Finally, using robust statistics, we show that the polynomials can be robustly estimated in the presence of moderate image noise and outliers. We conduct extensive simulations and real experiments to validate the performance of the new algorithm. The results demonstrate that RAS achieves notably higher accuracy than most existing robust motion-segmentation methods, including random sample consensus (RANSAC) and its variations. The implementation of the algorithm is also two to three times faster than the existing methods. The implementation of the algorithm and the benchmark scripts are available at http://perception.csl.illinois.edu/ras/. © 2010 The Author(s). |
Persistent Identifier | http://hdl.handle.net/10722/326810 |
ISSN | 2023 Impact Factor: 11.6 2023 SCImago Journal Rankings: 6.668 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Rao, Shankar R. | - |
dc.contributor.author | Yang, Allen Y. | - |
dc.contributor.author | Sastry, S. Shankar | - |
dc.contributor.author | Ma, Yi | - |
dc.date.accessioned | 2023-03-31T05:26:41Z | - |
dc.date.available | 2023-03-31T05:26:41Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | International Journal of Computer Vision, 2010, v. 88, n. 3, p. 425-446 | - |
dc.identifier.issn | 0920-5691 | - |
dc.identifier.uri | http://hdl.handle.net/10722/326810 | - |
dc.description.abstract | This paper studies segmentation of multiple rigid-body motions in a 3-D dynamic scene under perspective camera projection. We consider dynamic scenes that contain both 3-D rigid-body structures and 2-D planar structures. Based on the well-known epipolar and homography constraints between two views, we propose a hybrid perspective constraint (HPC) to unify the representation of rigid-body and planar motions. Given a mixture of K hybrid perspective constraints, we propose an algebraic process to partition image correspondences to the individual 3-D motions, called Robust Algebraic Segmentation (RAS). Particularly, we prove that the joint distribution of image correspondences is uniquely determined by a set of (2K)-th degree polynomials, a global signature for the union of K motions of possibly mixed type. The first and second derivatives of these polynomials provide a means to recover the association of the individual image samples to their respective motions. Finally, using robust statistics, we show that the polynomials can be robustly estimated in the presence of moderate image noise and outliers. We conduct extensive simulations and real experiments to validate the performance of the new algorithm. The results demonstrate that RAS achieves notably higher accuracy than most existing robust motion-segmentation methods, including random sample consensus (RANSAC) and its variations. The implementation of the algorithm is also two to three times faster than the existing methods. The implementation of the algorithm and the benchmark scripts are available at http://perception.csl.illinois.edu/ras/. © 2010 The Author(s). | - |
dc.language | eng | - |
dc.relation.ispartof | International Journal of Computer Vision | - |
dc.subject | Algebraic segmentation | - |
dc.subject | Epipolar geometry | - |
dc.subject | Homography | - |
dc.subject | Influence function | - |
dc.subject | Motion segmentation | - |
dc.subject | Outlier rejection | - |
dc.title | Robust algebraic segmentation of mixed rigid-body and planar motions from two views | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s11263-009-0314-1 | - |
dc.identifier.scopus | eid_2-s2.0-77951256374 | - |
dc.identifier.volume | 88 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 425 | - |
dc.identifier.epage | 446 | - |
dc.identifier.eissn | 1573-1405 | - |
dc.identifier.isi | WOS:000276429900005 | - |