709 citations · 4.7k across the 56 of their papers we have counts for
59 papers
Bayesian Multifractal Image Segmentation
Kareth M. León-López, Abderrahim Halimi, Jean-Yves Tourneret +1
Multifractal analysis (MFA) provides a framework for the global characterization of image textures by describing the spatial fluctuations of their local regularity based on the mul…
In-Flight Estimation of Instrument Spectral Response Functions Using Sparse Representations
Jihanne El Haouari, Jean-Michel Gaucel, Christelle Pittet +2
Accurate estimates of Instrument Spectral Response Functions (ISRFs) are crucial in order to have a good characterization of high resolution spectrometers. Spectrometers are compos…
HLoOP -- Hyperbolic 2-space Local Outlier Probabilities
Clémence Allietta, Jean-Philippe Condomines, Jean-Yves Tourneret +1
Hyperbolic geometry has recently garnered considerable attention in machine learning due to its capacity to embed hierarchical graph structures with low distortions for further dow…
A Robust and Flexible EM Algorithm for Mixtures of Elliptical Distributions with Missing Data
Florian Mouret, Alexandre Hippert-Ferrer, Frédéric Pascal +1
This paper tackles the problem of missing data imputation for noisy and non-Gaussian data. A classical imputation method, the Expectation Maximization (EM) algorithm for Gaussian m…
Reconstruction of Sentinel-2 Time Series Using Robust Gaussian Mixture Models -- Application to the Detection of Anomalous Crop Development in wheat and rapeseed crops
Florian Mouret, Mohanad Albughdadi, Sylvie Duthoit +3
Missing data is a recurrent problem in remote sensing, mainly due to cloud coverage for multispectral images and acquisition problems. This can be a critical issue for crop monitor…
Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series
Florian Mouret, Mohanad Albughdadi, Sylvie Duthoit +3
This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on…
Seeing Around Corners with Edge-Resolved Transient Imaging
Joshua Rapp, Charles Saunders, Julián Tachella +7
Non-line-of-sight (NLOS) imaging is a rapidly growing field seeking to form images of objects outside the field of view, with potential applications in search and rescue, reconnais…
Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review
Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez +6
The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or…
Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers
Julián Tachella, Yoann Altmann, Nicolas Mellado +5
Single-photon lidar has emerged as a prime candidate technology for depth imaging through challenging environments. Until now, a major limitation has been the significant amount of…
Bayesian 3D Reconstruction of Subsampled Multispectral Single-photon Lidar Signals
Julián Tachella, Yoann Altmann, Miguel Márquez +3
Light detection and ranging (Lidar) single-photon devices capture range and intensity information from a 3D scene. This modality enables long range 3D reconstruction with high rang…
Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images -- Extended Version
Corbineau Marie-Caroline, Kouamé Denis, Chouzenoux Emilie +2
Joint deconvolution and segmentation of ultrasound images is a challenging problem in medical imaging. By adopting a hierarchical Bayesian model, we propose an accelerated Markov c…
Bayesian 3D Reconstruction of Complex Scenes from Single-Photon Lidar Data
Julián Tachella, Yoann Altmann, Ximing Ren +4
Light detection and ranging (Lidar) data can be used to capture the depth and intensity profile of a 3D scene. This modality relies on constructing, for each pixel, a histogram of…
A Tensor Factorization Method for 3D Super-Resolution with Application to Dental CT
Janka Hatvani, Adrian Basarab, Jean-Yves Tourneret +2
Available super-resolution techniques for 3D images are either computationally inefficient prior-knowledge-based iterative techniques or deep learning methods which require a large…
A Fast Algorithm Based on a Sylvester-like Equation for LS Regression with GMRF Prior
Qi Wei, Emilie Chouzenoux, Jean-Yves Tourneret +1
This paper presents a fast approach for penalized least squares (LS) regression problems using a 2D Gaussian Markov random field (GMRF) prior. More precisely, the computation of th…
Partially Asynchronous Distributed Unmixing of Hyperspectral Images
Pierre-Antoine Thouvenin, Nicolas Dobigeon, Jean-Yves Tourneret
So far, the problem of unmixing large or multitemporal hyperspectral datasets has been specifically addressed in the remote sensing literature only by a few dedicated strategies. A…
Statistical modeling and classification of reflectance confocal microscopy images
Abdelghafour Halimi, Hadj Batatia, Jimmy Le Digabel +2
This paper deals with the characterization and classification of reflectance confocal microscopy images of human skin. The aim is to identify and characterize the lentigo, a phenom…
An unsupervised bayesian approach for the joint reconstruction and classification of cutaneous reflectance confocal microscopy images
Abdelghafour Halimi, Hadj Batatia, Jimmy Le Digabel +2
This paper studies a new Bayesian algorithm for the joint reconstruction and classification of reflectance confocal microscopy (RCM) images, with application to the identification…
A Bayesian Lower Bound for Parameter Estimation of Poisson Data Including Multiple Changes (extended)
Lucien Bacharach, Mohammed Nabil El Korso, Alexandre Renaux +1
This paper derives lower bounds for the mean square errors of parameter estimators in the case of Poisson distributed data subjected to multiple abrupt changes. Since both change l…
A Hierarchical Bayesian Model Accounting for Endmember Variability and Abrupt Spectral Changes to Unmix Multitemporal Hyperspectral Images
Pierre-Antoine Thouvenin, Nicolas Dobigeon, Jean-Yves Tourneret
Hyperspectral unmixing is a blind source separation problem which consists in estimating the reference spectral signatures contained in a hyperspectral image, as well as their rela…
EEG reconstruction and skull conductivity estimation using a Bayesian model promoting structured sparsity
Facundo Costa, Hadj Batatia, Thomas Oberlin +1
M/EEG source localization is an open research issue. To solve it, it is important to have good knowledge of several physical parameters to build a reliable head operator. Amongst t…
Unsupervised Nonlinear Spectral Unmixing based on a Multilinear Mixing Model
Qi Wei, Marcus Chen, Jean-Yves Tourneret +1
In the community of remote sensing, nonlinear mixing models have recently received particular attention in hyperspectral image processing. In this paper, we present a novel nonline…
R-FUSE: Robust Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation
Qi Wei, Nicolas Dobigeon, Jean-Yves Tourneret +2
This paper proposes a robust fast multi-band image fusion method to merge a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. Following t…
Multi-Band Image Fusion Based on Spectral Unmixing
Qi Wei, Jose Bioucas-Dias, Nicolas Dobigeon +3
This paper presents a multi-band image fusion algorithm based on unsupervised spectral unmixing for combining a high-spatial low-spectral resolution image and a low-spatial high-sp…
Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability
Pierre-Antoine Thouvenin, Nicolas Dobigeon, Jean-Yves Tourneret
Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing an hyperspectral image and their relative abundance fractions in each pixel. In practice,…