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Finite-element level-set curve particles

Publication ,  Journal Article
Jiang, T; Tomasi, C
Published in: Proceedings of the IEEE International Conference on Computer Vision
December 1, 2007

Particle filters encode a time-evolving probability density by maintaining a random sample from it. Level sets represent closed curves as zero crossings of functions of two variables. The combination of level sets and particle filters presents many conceptual advantages when tracking uncertain, evolving boundaries over time, but the cost of combining these two ideas seems prima facie prohibitive. A previous publication showed that a large number of virtual level set particles can be tracked with a logarithmic amount of work for propagation and update. We now make levelset curve particles more efficient by borrowing ideas from the Finite Element Method (FEM). This improves level-set curve particles in both running time (by a constant factor) and accuracy of the results. ©2007 IEEE.

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Published In

Proceedings of the IEEE International Conference on Computer Vision

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Publication Date

December 1, 2007
 

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Jiang, T., & Tomasi, C. (2007). Finite-element level-set curve particles. Proceedings of the IEEE International Conference on Computer Vision. https://doi.org/10.1109/ICCV.2007.4409184
Jiang, T., and C. Tomasi. “Finite-element level-set curve particles.” Proceedings of the IEEE International Conference on Computer Vision, December 1, 2007. https://doi.org/10.1109/ICCV.2007.4409184.
Jiang T, Tomasi C. Finite-element level-set curve particles. Proceedings of the IEEE International Conference on Computer Vision. 2007 Dec 1;
Jiang, T., and C. Tomasi. “Finite-element level-set curve particles.” Proceedings of the IEEE International Conference on Computer Vision, Dec. 2007. Scopus, doi:10.1109/ICCV.2007.4409184.
Jiang T, Tomasi C. Finite-element level-set curve particles. Proceedings of the IEEE International Conference on Computer Vision. 2007 Dec 1;

Published In

Proceedings of the IEEE International Conference on Computer Vision

DOI

Publication Date

December 1, 2007