MICHEL MENARD

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Papers by MICHEL MENARD

Radar UWB: Vision through a wall

HAL (Le Centre pour la Communication Scientifique Directe), Jul 1, 2008

Décrypter le vol des abeilles en 3D

HAL (Le Centre pour la Communication Scientifique Directe), 2016

Détection de signaux faibles dans des masses de données faiblement structurées

Recherche d’information, document et web sémantique =, 2019

L'étude présentée s'inscrit dans le cadre du développement d'une plateforme d'analyse automatique... more L'étude présentée s'inscrit dans le cadre du développement d'une plateforme d'analyse automatique de documents associée à un service sécurisé lanceurs d'alerte, de type GlobalLeaks. Cet article se focalise principalement sur la recherche de signaux faibles présents dans les documents. Il s'agit d'une problématique investiguée dans un grand nombre de champs disciplinaires et de cadres applicatifs. Nous supposons que chaque document est un mélange d'un petit nombre de thèmes ou catégories, et que la création de chaque mot est attribuable en termes de probabilités à l'un des thèmes du document. Les catégories des documents transmis ne sont pas connues a priori. Les mots-clés présents dans les documents représentatifs de ces catégories sont également inconnus. L'analyse des documents reçus doit simultanément permettre de découvrir les thèmes, classer les documents relativement à ces thèmes, détecter les mots-clés pertinents relatifs aux thèmes et enfin découvrir les mots-clés relevant d'un thème "signal faible" éventuel. Pour atteindre cet objectif, nous proposons une définition du signal faible qui conditionne l'approche conjointe modèle thématique / plongement lexical, et contraint le choix des méthodes LDA et Word2Vec. Nous proposons d'évaluer les partitions obtenues grâce à un indice de cohérence sur la collection de mots représentative de chaque thème obtenu. Les clusters obtenus sont ainsi plus cohérents au sens contextuel. La détection du cluster associé au signal faible est alors plus aisée et plus pertinente.

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Decomposition of Dynamic Textures Using Morphological Component Analysis

IEEE Transactions on Circuits and Systems for Video Technology, Feb 1, 2012

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Validation of Echo-PIV method through PIV comparisons in the assessment of ventricular flows

Computer Methods in Biomechanics and Biomedical Engineering, Aug 1, 2011

Colour Echo-Doppler is a non-invasive ultrasound method routinely used to assess ventricular velo... more

Morphological Component Analysis for Decomposing Dynamic Textures

Springer proceedings in mathematics, 2011

ABSTRACT

Fusion of heterogeneous and noisy informations: application to the quantification of the coronary stenosis

ABSTRACT

Abstract 19086: Validation Against Laser Particular Velocity Imaging of a New Algorithm of Echo Contrast Tracking Based on Optical Flow Algorithm for Echo Piv Method

Circulation, Nov 23, 2010

Background: Like optical Particular Velocity Imaging (PIV) analysis, Echo-PIV (EPIV) is usually b... more Background: Like optical Particular Velocity Imaging (PIV) analysis, Echo-PIV (EPIV) is usually based on a crosscorrelation technique. Although preliminary studies have shown high spatial resolution, a small dynamic range limits this technique. We have recently described a new method based on Optical flow algorithm applied to corrected raw B mode backscattered images (digital format prior to scan conversion). We thought validate the accuracy of this method againts PIV to describe intraventricular complex flow fields in experimental conditions. Methods: In vitro model: Atrio-ventricular simulator with a bioprostheses in mitral position. Stroke volume range from 30 to 70 ml, Frame rate (FR) from 178 to 530 i/s. Echo contrast concentration was kept stable along the experimentation Mechanical index :0.21. Image processing. Pre-treatment (1) Removing noise through soft wavelet thresholding method. (2) Decomposition of echo-piv sequence into: (a) geometrical component, (b) textural component (b) and © noise. (3...

Robotic and document analysis cross-fertilization: Improving place cells based robot navigation

This paper proposes a place cell model allowing place recognition in the context of robot autonom... more This paper proposes a place cell model allowing place recognition in the context of robot autonomous navigation. The robustness of this approach lies in the fact that even if one or several patterns characterizing the place are removed or not visible anymore, a place can still be recognized. The recognition process in this work is improved with respect to the state-of-theart place cells approach. Additionally, the interconnection of the modules is made such that the robot is able to learn new places as it navigates and interacts with the environment to get to its final destination. Experimental results validate the advantage of the incremental learning allowing the robot to cope with any unforeseen changes and thus adapting itself to the environment.

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A Meaningful Information Extraction System for Interactive Analysis of Documents

This paper is related to a project aiming at discovering weak signals from different streams of i... more This paper is related to a project aiming at discovering weak signals from different streams of information, possibly sent by whistleblowers. The study presented in this paper tackles the particular problem of clustering topics at multi-levels from multiple documents, and then extracting meaningful descriptors, such as weighted lists of words for document representations in a multi-dimensions space. In this context, we present a novel idea which combines Latent Dirichlet Allocation and Word2vec (providing a consistency metric regarding the partitioned topics) as potential method for limiting the "a priori" number of cluster K usually needed in classical partitioning approaches. We proposed 2 implementations of this idea, respectively able to: (1) finding the best K for LDA in terms of topic consistency; (2) gathering the optimal clusters from different levels of clustering. We also proposed a non-traditional visualization approach based on a multi-agents system which combines both dimension reduction and interactivity.

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Fuzzy clustering and switching regression models using ambiguity and distance rejects

Fuzzy Sets and Systems, Sep 1, 2001

This paper examines how reject options can be used in performing fuzzy clustering and switching r... more This paper examines how reject options can be used in performing fuzzy clustering and switching regression models. We deÿne an objective function in which reject options are introduced to optimization of certain clustering models. This approach can be directly applied to any clustering model which can be represented as a functional dependent upon a set of cluster centers. The approach can be further generalized for models that require parameters other than the cluster centers. Two types of reject have been included: (1) the ambiguity reject which concerns patterns lying near the cluster boundaries or in the case of switching regression problems, the data points which ÿt several models equally well; (2) the distance or error reject dealing with patterns that are far away from all the clusters. Clustering and fuzzy c-regression algorithms such as FcM (fuzzy c-means) and FcRM (fuzzy c-regression models) which use calculus-based optimization methods su er from several drawbacks. They are very sensitive to the presence of noise. Moreover, the memberships are relative numbers. The membership of a point in a cluster depends on the membership of the point in all other clusters. So, the cluster centers or estimates for the parameters are poor. This can be a serious problem in situations where one wishes to generate membership functions from training data. This paper provides answers to these problems: to avoid the memberships to be spread across the clusters and to allow the distinction between "equally likely" and "unknown", we deÿne partial ambiguity rejects which introduce a discounting process between the classical FcM or FcRM membership functions; to improve the performance of our algorithm in the presence of noise, we use an amorphous noise cluster deÿned in Demko et al. (Actes des sixiÂ emes rencontres del la sociÃ etÃ e francophone de classiÿcation, Montpellier, France, September 1998). To compute these rejects, we propose an extension of FcRM algorithm (Hathaway and Bezdek, IEEE Trans. Fuzzy Systems 1 (3) (1993) 195-203). This algorithm is called the fuzzy (c+2)-regression model (Fc+2RM). Preliminary computational experiences on the developed algorithm are encouraging and compare favorably with results from other methods as FcRM and AFC algorithms on the same data sets.

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Using the bee (Apis Mellifica) for bio-monitoring agricultural pollution (pesticides). Case study via use of a video based bee counter for remote monitoring and real-time mortality in colonies

HAL (Le Centre pour la Communication Scientifique Directe), 2012

Using the bee (Apis Mellifica) for bio-monitoring agricultural pollution (pesticides). Case study... more

Tagged Cardiac MRI: A New Diffusion Approach to Increase The Robustness of The Detection and The Following of The Grid of Tags

HAL (Le Centre pour la Communication Scientifique Directe), Jun 1, 2004

Détection et suivi des déformations intra-myocardiques en IRM cardiaque marquée

HAL (Le Centre pour la Communication Scientifique Directe), Sep 1, 2003

Assessment of 2D velocity field using contrast echo. Comparison of Weiner filtering and cross-correlation method

HAL (Le Centre pour la Communication Scientifique Directe), Dec 6, 2006

Selective Image Diffusion for Oriented Pattern Extraction

Anisotropic regularization PDE's (Partial Differential Equation) raised a strong interest in the ... more Anisotropic regularization PDE's (Partial Differential Equation) raised a strong interest in the field of image processing. The benefit of PDE-based regularization methods lies in the ability to smooth data in a nonlinear way, allowing the preservation of important image features (contours, corners or other discontinuities). In this article, a selective diffusion approach based on the framework of Extreme Physical Information theory is presented. It is shown that this particular framework leads to a particular regularization PDE which makes it possible integration of prior knowledge within diffusion scheme. As a proof a feasibility, results of oriented pattern extractions are presented on ad hoc images. This approach may find applicability in vision in robotics.

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Using CFAR algorithm to further improve a combined through-wall imaging method

European Signal Processing Conference, Oct 18, 2012

This paper presents a novel way to increase the detection/localization performance of a through-t... more This paper presents a novel way to increase the detection/localization performance of a through-the-wall radar. In one hand, the false negative detection is reduced by combining the backprojection and the trilateration algorithms. In the other hand, the false positive detection is reduced by using a Constant False Alarm Rate (CFAR) algorithm. Hence, we show that the detection/localization performance of the resulting imaging module is improved. The whole processing pipeline of our ultra wideband (UWB) multistatic pulse radar system is described. We specifically focus on the combination of backprojection and trilateration and the use of CFAR algorithm. Simulations and experiments indicate that our combined method outperforms the other presented method.

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<title>Mass functions assessment: case of multiple hypothesis for the evidential approach</title>

Proceedings of SPIE, Dec 17, 1996

ABSTRACT The purpose of this paper is to define the mass functions for a set of multiple mixed hy... more ABSTRACT The purpose of this paper is to define the mass functions for a set of multiple mixed hypothesis in an context of Dempster/Shafer (DS) theory which offers an interesting tool to combine data providing from heterogeneous sources more or less reliable by managing imprecision and uncertainty. This is particularly important when dealing with multi-modality imaging (satellite image), where the fusion of information increases the global knowledge about the phenomenon while decreasing the imprecision and uncertainty about it. This theory also enables us to assign masses to 2D elements (D: decision space) rather than to D elements as in probabilistic theory. The DS has been used in many applications in the field of image analysis, but without its all powerful. When using with only simple hypothesis (an object belongs to only one class), the theory falls in the probabilistic case, which is considered as a particular case. Bloch and Barnett attempt to use double hypothesis but their method still remains particular and restrictive. We propose in this paper a method to extract for a class the consonance and dissonance degrees among several classifiers (methods), and the integration of these terms to initialize the mass functions with multiple mixed hypothesis in order to use the orthogonal Dempster/Shafer Rule. The problem must be viewed from multiclass, multi-sources (images) and multi- point of view (methods or classifiers used) context. We first show how our method works with 1 -- image, 2 -- classifiers, and 2 -- hypothesis and then generalize for P - - images, K -- sources and 2D -- hypothesis.

Predation of the invasive Asian hornet affects foraging activity and survival probability of honey bees in Western Europe

Journal of Pest Science, Nov 21, 2018

Introduced in France more than a decade ago from China, the invasive Asian hornet Vespa velutina ... more Introduced in France more than a decade ago from China, the invasive Asian hornet Vespa velutina preys on honey bee Apis mellifera foragers at hive entrances and is a major concern for Western European beekeepers and governmental policies. Asian hornet predation is suspected to weaken honey bee colonies before the winter season. In this study, we assessed the risk of winter colony losses related to hornet-induced disturbances by combining field observations and model system simulations. We provide empirical evidence in bee foragers' homing failures and bee foraging paralysis behaviour of the colony related to the predator-prey relationships between the hornet and the honey bees nearby colonies' entrances. Our modelbased assessment confirms concerns of beekeepers and governmental policies that these hornet-induced disturbances affect honey bee colony dynamics and winter survival. Simulations reveal that the foraging paralysis behavioural response of honey bee colonies is an important mechanism underlying winter colony collapse. We provide recommendations of beekeeping management to mitigate potential detrimental effects from hornets to ensure bee colony survival, such as the control of the hornet-induced foraging paralysis of Western European honey bee colonies that may be viewed as an unadapted behavioural response to the invasive predator.

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Coalescence floue avec ambiguïté pour la fusion multi-classifieurs : coopération classification-classement

HAL (Le Centre pour la Communication Scientifique Directe), Feb 1, 2000

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