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Audrey Giremus

5 accepted papers

2019

Tracking a Cluster of Space Debris in Low Orbit by Filtering on Lie Groups

ICASSP 2019accepted

This paper addresses the problem of tracking a cluster of space debris sufficiently close to each other to be considered as a single ex-tended object. State-of-the-art random-matrix methods estimate the kinematics of the object centroid by assuming that its shape is elliptic and that the observation…

Cited by 0SourceScholar
2018

Generative Model and Associated Metric for Coordinated-Motion Target Groups

ICASSP 2018accepted

In multi-object tracking, some target groups can share a coordinated motion. They can for instance form a convoy or follow a road network. In any case, the target trajectories can be modeled by using the group motion characteristics and the self-properties of the targets. For this purpose, we introd…

Cited by 0SourceScholar
2017

A generalized Swendsen-Wang algorithm for Bayesian nonparametric joint segmentation of multiple images

ICASSP 2017accepted

A generalized Swendsen-Wang (GSW) algorithm is proposed for the joint segmentation of a set of multiple images sharing, in part, an unknown number of common classes. The class labels are a priori modeled by a combination of the hierarchical Dirichlet process (HDP) and the Potts model. The HDP allows…

Cited by 0SourceScholar
2017

Bernoulli filter based algorithm for joint target tracking and classification in a cluttered environment

ICASSP 2017accepted

In this paper, single-target tracking using radar measurements is addressed. Recently, algorithms based on Bernoulli random finite sets have proved efficient in a cluttered environment. However, in Bayesian approaches, the choice of the motion model impacts the trajectory estimation accuracy. To sel…

Cited by 0SourceScholar
2015

Potts model parameter estimation in Bayesian segmentation of piecewise constant images

ICASSP 2015accepted

The paper presents a method for estimating the parameter of a Potts model jointly with the unknowns of an image segmentation problem. The method addresses piecewise constant images degraded by additive noise. The proposed solution follows a Bayesian approach, that yields the posterior law for all th…

Cited by 0SourceScholar