@inproceedings{ecfd5002a2674073b96a4622bd8456ea,
title = "Signals and images foreground/background joint estimation and separation",
abstract = "This paper is devoted to a foreground/background joint estimation and separation problem. We first observe that this problem is modeled by a conditionally linear and Gaussian hidden Markov chain (CLGHMC). We next propose a filtering algorithm in the general non-linear and non Gaussian conditionally hidden Markov chain (CHMC), allowing the propagation of the filtering densities associated to the foreground and the background. We then focus on the particular case of our CLGHMC in which these filtering densities are weighted sums of Gaussian distributions; the parameters of each Gaussian are computed by using the Kalman filter algorithm, while the weights are computed by using the particle filter algorithm. We finally perform some simulations to highlight the interest of our method in both signals and images foreground/backgound joint estimation and separation.",
keywords = "Conditionally hidden Markov chain models, Foreground/background joint estimation and separation, Kalman filter, Particle filter",
author = "Boujemaa Ait-El-Fquih and Ali Mohammad-Djafari",
year = "2010",
doi = "10.1063/1.3573626",
language = "English (US)",
isbn = "9780735408609",
series = "AIP Conference Proceedings",
pages = "266--273",
booktitle = "Bayesian Inference and Maximum Entropy Methods in Science and Engineering - Proc. of the 30th Intl. Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2010",
note = "30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2010 ; Conference date: 04-07-2010 Through 09-07-2010",
}