Search Results for author: Sylvain Meignen

Found 6 papers, 3 papers with code

Gridless 2D Recovery of Lines using the Sliding Frank-Wolfe Algorithm

no code implementations18 Mar 2024 Kévin Polisano, Basile Dubois-Bonnaire, Sylvain Meignen

We present a new approach leveraging the Sliding Frank--Wolfe algorithm to address the challenge of line recovery in degraded images.

Line Detection

Instantaneous Frequency Estimation in Multicomponent Signals in Case of Interference Based on the Prony Method

1 code implementation22 Dec 2023 Basile Dubois-Bonnaire, Sylvain Meignen, Kévin Polisano

In this paper, we develop a general method to estimate the instantaneous frequencies of the modes making up a multicomponent signal when the former exhibit interference in the time-frequency plane.

Unsupervised classification of the spectrogram zeros

1 code implementation11 Oct 2022 Juan M. Miramont, François Auger, Marcelo A. Colominas, Nils Laurent, Sylvain Meignen

In this work, a classification of these zeros in three types is introduced, based on the nature of the components that interfere to produce them.

Classification Denoising

Instantaneous Frequency Estimation In Multi-Component Signals Using Stochastic EM Algorithm

no code implementations28 Mar 2022 Quentin Legros, Dominique Fourer, Sylvain Meignen, Marcelo A. Colominas

This paper addresses the problem of estimating the modes of an observed non-stationary mixture signal in the presence of an arbitrary distributed noise.

A Novel Ridge Detector for Nonstationary Multicomponent Signals: Development and Application to Robust Mode Retrieval

1 code implementation28 Sep 2020 Nils Laurent, Sylvain Meignen

Time-frequency analysis is often used to study non stationary multicomponent signals, which can be viewed as the surperimposition of modes, associated with ridges in the TF plane.

Retrieval

Robust 3D reconstruction of dynamic scenes from single-photon lidar using Beta-divergences

no code implementations20 Apr 2020 Quentin Legros, Julian Tachella, Rachael Tobin, Aongus McCarthy, Sylvain Meignen, Gerald S. Buller, Yoann Altmann, Stephen McLaughlin, Michael E. Davies

In this work, we consider a new similarity measure for robust depth estimation, which allows us to use a simple observation model and a non-iterative estimation procedure while being robust to mis-specification of the background illumination model.

3D Reconstruction Depth Estimation

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