Particle filtering for computer vision-based identification of frame model parameters
Abstract
In this paper we present a new approach for solving identification problems based on a novel combination of computer vision techniques, Bayesian state estimation and finite element method. Using our approach we solved two identification problems for a laboratory-scale aluminum frame. In the first problem, we recursively estimated the elastic modulus of the frame material. In the second problem, for the known elastic constant, we identified sequentially the position of a quasi-static concentrated load.
Keywords
identification problems, Bayesian state estimation, particle filtering, computer vision, digital image correlation, finite element method,References
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Published
Jan 25, 2017
How to Cite
TEKIELI, Marcin; SŁOŃSKI, Marek.
Particle filtering for computer vision-based identification of frame model parameters.
Computer Assisted Methods in Engineering and Science, [S.l.], v. 21, n. 1, p. 39-48, jan. 2017.
ISSN 2956-5839.
Available at: <https://cames-old.ippt.pan.pl/index.php/cames/article/view/53>. Date accessed: 26 apr. 2025.
doi: http://dx.doi.org/10.24423/cames.53.
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