Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods-Reference-Cited by-同舟云学术

Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods

Published:2006-12-28 Issue:1 Volume:74 Page:75-102
ISSN:0920-5691
Container-title:International Journal of Computer Vision
language:en
Short-container-title:Int J Comput Vision

Author:

Arnaud Elise,Mémin Etienne

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s11263-006-0003-2.pdf

Reference58 articles.

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2. Arnaud, E. and Mémin, E. 2004. Optimal importance sampling for tracking in image sequences: application to point tracking. In European Conference on Computer Vision, Vol. 3, pp. 302–314.

3. Arnaud, E., Mémin, E., and Cernuschi-Frías, B. 2005. Conditional filters for image sequence based tracking—application to point tracking. IEEE Transactions of Image Processing, 14(1):63–79.

4. Arulampalam, M.S., Maskell, S., Gordon, N., and Clapp, T. 2002. A tutorial on particles filters for online Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2):174–18.

5. Aschwanden, P. and Guggenbül, W. 1992. Experimental results from a comparative study on correlation type registration algorithms. In Robust Computer Vision: Quality of Vision Algorithms, Förstner and Ruwiedel, (Ed.), Wichmann, pp. 268–282.

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