Search results for: Audrey Giremus

Items from 1 to 20 out of 37 results

article

Variance component analysis to assess protein quantification in biomarker validation: application to selected reaction monitoring-mass spectrometry

Amna Klich, Catherine Mercier, Laurent Gerfault, Pierre Grangeat, more

BMC Bioinformatics > 2018 > 19 > 1 > 1-12

Background In the field of biomarker validation with mass spectrometry, controlling the technical variability is a critical issue. In selected reaction monitoring (SRM) measurements, this issue provides the opportunity of using variance component analysis to distinguish various sources of variability. However, in case of unbalanced data (unequal number of observations in all factor combinations),...

article

Linear MALDI-ToF simultaneous spectrum deconvolution and baseline removal

Vincent Picaud, Jean-Francois Giovannelli, Caroline Truntzer, Jean-Philippe Charrier, more

BMC Bioinformatics > 2018 > 19 > 1 > 1-20

Background Thanks to a reasonable cost and simple sample preparation procedure, linear MALDI-ToF spectrometry is a growing technology for clinical microbiology. With appropriate spectrum databases, this technology can be used for early identification of pathogens in body fluids. However, due to the low resolution of linear MALDI-ToF instruments, robust and accurate peak picking remains a challenging...

chapter

Jeffrey's divergence between autoregressive moving-average processes

Leo Legrand, Eric Grivel, Audrey Giremus

2017 25th European Signal Processing Conference (EUSIPCO) > 1085 - 1089

2017 25th European Signal Processing Conference (EUSIPCO)

Various works have been carried out about the Jeffrey's divergence (JD) which is the symmetric version of the Kullback-Leibler (KL) divergence. An expression of the JD for Gaussian processes can be deduced from the definition of the KL divergence and the expression of the Gaussian-multivariate distributions of k-dimensional random vectors. It depends on the k × k Toeplitz covariance matrices of the...

article

Bayesian inference for biomarker discovery in proteomics: an analytic solution

Noura Dridi, Audrey Giremus, Jean-Francois Giovannelli, Caroline Truntzer, more

EURASIP Journal on Bioinformatics and Systems Biology > 2017 > 2017 > 1 > 1-14

This paper addresses the question of biomarker discovery in proteomics. Given clinical data regarding a list of proteins for a set of individuals, the tackled problem is to extract a short subset of proteins the concentrations of which are an indicator of the biological status (healthy or pathological). In this paper, it is formulated as a specific instance of variable selection. The originality is...

chapter

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

Leo Legrand, Audrey Giremus, Eric Grivel, Laurent Ratton, more

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) > 4396 - 4400

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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 select an appropriate set of motion models, a joint tracking and classification (JTC) algorithm can be...

chapter

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

Jessica Sodjo, Audrey Giremus, Nicolas Dobigeon, Jean-Francois Giovannelli

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) > 1882 - 1886

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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 the number of regions in each image and classes to be automatically inferred while the Potts model...

article

Bayesian non-parametric methods for dynamic state-noise covariance matrix estimation: Application to target tracking

Clément Magnant, Audrey Giremus, Eric Grivel, Laurent Ratton, more

Signal Processing > 2016 > 127 > C > 135-150

When using Bayesian estimation techniques, the algorithm is strongly sensitive to the system evolution model and more particularly to the setting of the state-noise covariance matrix. Recently, Bayesian non-parametric models and in particular Dirichlet processes (DPs) have been proposed as a scalable solution to this issue. They assume that the system can switch between an infinite number of state-space...

chapter

A Bayesian non parametric time-switching autoregressive model for multipath errors in GPS navigation

Audrey Giremus, Vincent Pereira

2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) > 1 - 5

2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)

Multipath is one of the most penalizing error sources in GPS navigation. It occurs when the satellite signals are reflected on obstacles before reaching the GPS receiver, corrupting the satellite-receiver distance measurements. In recent works, Bayesian non parametric (BNP) models of the measurement errors in the presence of multipath were considered. The latter were assumed to be distributed according...

chapter

Joint segmentation of multiple images with shared classes: A Bayesian nonparametrics approach

Jessica Sodjo, Audrey Giremus, Francois Caron, Jean-Francois Giovannelli, more

2016 IEEE Statistical Signal Processing Workshop (SSP) > 1 - 5

2016 IEEE Statistical Signal Processing Workshop (SSP)

A combination of the hierarchical Dirichlet process (HDP) and the Potts model is proposed for the joint segmentation/classification of a set of images with shared classes. Images are first divided into homogeneous regions that are assumed to belong to the same class when sharing common characteristics. Simultaneously, the Potts model favors configurations defined by neighboring pixels belonging to...

chapter

Multi-target tracking using a PHD-based joint tracking and classification algorithm

Clement Magnant, Audrey Giremus, Eric Grivel, Laurent Ratton, more

2016 IEEE Radar Conference (RadarConf) > 1 - 6

2016 IEEE Radar Conference (RadarConf16)

When using Bayesian estimation techniques for target tracking, the algorithm accuracy is induced by the choice of the system evolution model. Information on the type of target and its maneuver capability can then be helpful to choose relevant motion models. Joint tracking and classification (JTC) methods based on target features have thus been introduced. Among them, we recently proposed to take into...

article

From Intrinsic Optimization to Iterated Extended Kalman Filtering on Lie Groups

Guillaume Bourmaud, Rémi Mégret, Audrey Giremus, Yannick Berthoumieu

Journal of Mathematical Imaging and Vision > 2016 > 55 > 3 > 284-303

In this paper, we propose a new generic filter called Iterated Extended Kalman Filter on Lie Groups. It allows to perform parameter estimation when the state and the measurements evolve on matrix Lie groups. The contribution of this work is threefold: (1) the proposed filter generalizes the Euclidean Iterated Extended Kalman Filter to the case where both the state and the measurements evolve on Lie...

chapter

Joint tracking and classification based on kinematic and target extent measurements

Clement Magnant, Audrey Giremus, Eric Grivel, Laurent Ratton, more

2015 18th International Conference on Information Fusion (Fusion) > 1748 - 1755

2015 18th International Conference on Information Fusion (Fusion)

A great deal of interest has been paid to target tracking for the last decades. When using Bayesian estimation algorithms, choosing relevant motion models is crucial for accurate localization. Information on the type of target and its maneuver capability can be helpful in the motion model design. Thus, joint tracking and classification (JTC) methods based on target features have been recently developed...

chapter

Dirichlet-process-mixture-based Bayesian nonparametric method for Markov switching process estimation

Clement Magnant, Audrey Giremus, Eric Grivel, Laurent Ratton, more

2015 23rd European Signal Processing Conference (EUSIPCO) > 1969 - 1973

2015 23rd European Signal Processing Conference (EUSIPCO)

Dirichlet process (DP) mixtures were recently introduced to deal with switching linear dynamical models (SLDM). They assume the system can switch between an a priori infinite number of state-space representations (SSR) whose parameters are on-line inferred. The estimation problem can thus be of high dimension when the SSR matrices are unknown. Nevertheless, in many applications, the SSRs can be categorized...

chapter

Classifying autoregressive models using dissimilarity measures: A comparative study

Clement Magnant, Eric Grivel, Audrey Giremus, Laurent Ratton, more

2015 23rd European Signal Processing Conference (EUSIPCO) > 998 - 1002

2015 23rd European Signal Processing Conference (EUSIPCO)

Autoregressive (AR) models are used in various applications, from speech processing to radar signal analysis. In this paper, our purpose is to extract different model subsets from a set of two or more AR models. The approach operates with the following steps: firstly the matrix composed of dissimilarity measures between AR-model pairs are created. This can be based on the symmetric Itakura divergence,...

chapter

Nonlinear filtering approaches for INS/GPS integration

Audrey Giremus, Arnaud Doucet, Anne-Christine Escher, Jean-Yves Tourneret

2004 12th European Signal Processing Conference > 873 - 876

2004 12th European Signal Processing Conference

Navigation with an integrated INS/GPS approach requires to solve a set of nonlinear equations. In this case, nonlinear filtering techniques such as Particle Filtering methods are expected to perform better than the classical, but suboptimal, Extended Kalman Filter. Besides, the INS/GPS model has a conditionally linear Gaussian structure. A Rao-Blackwellization procedure can then be applied to reduce...

chapter

Potts model parameter estimation in Bayesian segmentation of piecewise constant images

Roxana-Gabriela Rosu, Jean-Francois Giovannelli, Audrey Giremus, Cornelia Vacar

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) > 4080 - 4084

ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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 the unknowns (labels, gray levels, noise level and Potts parameter). It is explored by means of MCMC stochastic...

article

On Computing Jeffrey’s Divergence Between Time-Varying Autoregressive Models

Clement Magnant, Audrey Giremus, Eric Grivel

IEEE Signal Processing Letters > 2015 > 22 > 7 > 915 - 919

Autoregressive (AR) and time-varying AR (TVAR) models are widely used in various applications, from speech processing to biomedical signal analysis. Various dissimilarity measures such as the Itakura divergence have been proposed to compare two AR models. However, they do not take into account the variances of the driving processes and only apply to stationary processes. More generally, the comparison...

article

Continuous-Discrete Extended Kalman Filter on Matrix Lie Groups Using Concentrated Gaussian Distributions

Guillaume Bourmaud, Rémi Mégret, Marc Arnaudon, Audrey Giremus

Journal of Mathematical Imaging and Vision > 2015 > 51 > 1 > 209-228

In this paper we generalize the continuous-discrete extended Kalman filter (CD-EKF) to the case where the state and the observations evolve on connected unimodular matrix Lie groups. We propose a new assumed density filter called continuous-discrete extended Kalman filter on Lie groups (CD-LG-EKF). It is built upon a geometrically meaningful modeling of the concentrated Gaussian distribution on Lie...

article

Sequential beat-to-beat P and T wave delineation and waveform estimation in ECG signals: Block Gibbs sampler and marginalized particle filter

Chao Lin, Georg Kail, Audrey Giremus, Corinne Mailhes, more

Signal Processing > 2014 > 104 > Complete > 174-187

For ECG interpretation, the detection and delineation of P and T waves are challenging tasks. This paper proposes sequential Bayesian methods for simultaneous detection, threshold-free delineation, and waveform estimation of P and T waves on a beat-to-beat basis. By contrast to state-of-the-art methods that process multiple-beat signal blocks, the proposed Bayesian methods account for beat-to-beat...

chapter

Classifying several state models using Jeffrey's divergence: Application to target tracking

Clement Magnant, Eric Grivel, Audrey Giremus, Bernard Joseph, more

2014 International Radar Conference > 1 - 6

2014 International Radar Conference (Radar)

One of the most important challenges when applying multiple-model approaches is model-set design. However, to our knowledge, there are not general rules to choose models. For this purpose, we recently proposed an approach based on the Jeffrey's divergence to characterize dissimilarities between two state models. In this paper, our contribution is to use the Jeffrey's divergence to classify at least...

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ESTIMATION (10)
BAYES METHODS (7)
PARTICLE FILTERING (7)
GLOBAL POSITIONING SYSTEM (6)
NOISE MEASUREMENT (6)
TARGET TRACKING (6)
COMPUTATIONAL MODELING (5)
DIRICHLET PROCESS (5)
MATHEMATICAL MODEL (5)
MULTIPATH (5)
NOISE (5)
GPS NAVIGATION (4)
MODEL SELECTION (4)
MULTIPLE MODELS (4)
PARTICLE FILTER (4)
RECEIVERS (4)
SATELLITES (4)
VECTORS (4)
ANALYTICAL MODELS (3)
COVARIANCE MATRICES (3)
EUROPE (3)
INTERFERENCE (3)
JEFFREY'S DIVERGENCE (3)
MASS SPECTROMETRY (3)
POTTS MODEL (3)
RAO-BLACKWELLIZED PARTICLE FILTER (3)
SIGNAL PROCESSING (3)
BAYESIAN ESTIMATION (2)
BAYESIAN NON-PARAMETRIC METHODS (2)
BAYESIAN NONPARAMETRICS (2)
CLASSIFICATION (2)
CORRELATION (2)
DIRICHLET PROCESS MIXTURES (2)
EIGENVALUES AND EIGENFUNCTIONS (2)
EQUATIONS (2)
EVIDENCE (2)
EXTENDED KALMAN FILTER (2)
FAULT DIAGNOSIS (2)
IMAGE SEGMENTATION (2)
INERTIAL NAVIGATION (2)
ITAKURA DIVERGENCE (2)
KALMAN FILTERING (2)
KINEMATICS (2)
LIE GROUPS (2)
MANIFOLDS (2)
MATRIX DECOMPOSITION (2)
MOBILE COMMUNICATION (2)
NAVIGATION (2)
PARTICLE FILTERING (NUMERICAL METHODS) (2)
PROTEOMICS (2)
RADAR TRACKING (2)
RAO-BLACKWELLIZATION (2)
SEQUENTIAL MONTE CARLO METHODS (2)
TARGET EXTENT (2)
UNSCENTED KALMAN FILTER (2)
3D SIMILARITY (1)
APPROXIMATION METHODS (1)
ATOMIC MEASUREMENTS (1)
AUTOREGRESSIVE MODEL (1)
AUTOREGRESSIVE MOVING-AVERAGE PROCESSES (1)
AUTOREGRESSIVE PROCESS (1)
BASELINE (1)
BAYES (1)
BAYESIAN APPROACH (1)
BAYESIAN INFERENCE (1)
BAYESIAN NON PARAMETRIC METHODS (1)
BAYESIAN STRATEGY (1)
BBIOMEDICAL MONTIORING (1)
BEAT-TO-BEAT ANALYSIS (1)
BERNOULLI FILTER (1)
BIOLOGICAL CELLS (1)
BIOLOGICAL SYSTEM MODELING (1)
BIOMARKER (1)
BIOMEDICAL MEASUREMENT (1)
BIOMEDICAL SIGNAL PROCESSING (1)
BOLTZMANN MACHINE (1)
CAMERA POSE (1)
CAMERA POSE FILTERING (1)
CELLS (BIOLOGY) (1)
CHROMATOGRAPHY (1)
CLASSIFICATION ALGORITHMS (1)
COGNITIVE RADIO (1)
COMBINING GENERALIZED LIKELIHOOD RATIO (1)
COMPUTATIONAL COMPLEXITY (1)
COPULAS (1)
COVARIANCE ANALYSIS (1)
COVARIANCE MATRIX (1)
COVARIANCE MATRIX ESTIMATION (1)
DECONVOLUTION (1)
DELAYED SAMPLING (1)
DISCRETE TIME FILTERING (1)
ECG (1)
ECG P AND T WAVE DELINEATION (1)
EXPERIMENTAL DESIGN (1)
FAULT MEASUREMENT DETECTION (1)
FILTERING (1)
FILTERING ON MANIFOLD (1)
FILTERING ON MANIFOLDS (1)
FIXED-LAG PARTICLE FILTER (1)
GAUSS-NEWTON (1)
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Search results for: Audrey Giremus

Variance component analysis to assess protein quantification in biomarker validation: application to selected reaction monitoring-mass spectrometry

Linear MALDI-ToF simultaneous spectrum deconvolution and baseline removal

Jeffrey's divergence between autoregressive moving-average processes

Bayesian inference for biomarker discovery in proteomics: an analytic solution

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

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

Bayesian non-parametric methods for dynamic state-noise covariance matrix estimation: Application to target tracking

A Bayesian non parametric time-switching autoregressive model for multipath errors in GPS navigation

Joint segmentation of multiple images with shared classes: A Bayesian nonparametrics approach

Multi-target tracking using a PHD-based joint tracking and classification algorithm

From Intrinsic Optimization to Iterated Extended Kalman Filtering on Lie Groups

Joint tracking and classification based on kinematic and target extent measurements

Dirichlet-process-mixture-based Bayesian nonparametric method for Markov switching process estimation

Classifying autoregressive models using dissimilarity measures: A comparative study

Nonlinear filtering approaches for INS/GPS integration

Potts model parameter estimation in Bayesian segmentation of piecewise constant images

On Computing Jeffrey’s Divergence Between Time-Varying Autoregressive Models

Continuous-Discrete Extended Kalman Filter on Matrix Lie Groups Using Concentrated Gaussian Distributions

Sequential beat-to-beat P and T wave delineation and waveform estimation in ECG signals: Block Gibbs sampler and marginalized particle filter

Classifying several state models using Jeffrey's divergence: Application to target tracking

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