Adaptative Tracking of Non Rigid Objects Based on Color Histograms and Automatic Parameter Selection

International audienceOne of the main difficulties in visual tracking is to take into account appearance changes (not only of the target but also of or due to the scene, illumination for example). The use of a Bayesian framework is very flexible and has proven to be very efficient in visual tracking. Moreover, color or greylevel histograms allow to track an objet with a low computational cost. The recently proposed color-based trackers integrated in a probabilistic framework are efficient for a given application (face tracking for example) but can not be generalized easily, due to the initialization and the adjustment of the different tracker parameters that are dependent on the input sequence. This paper presents a method based on color integrated in a particle filter that allows to cope with some of the usual problems of visual tracking (occlusions, target appearance changes, changes in resolution or in illumination) and to adapt easily to different applications (tracking of structures in aerial imagery as well as football players). The novelty of the tracker is its ability to automatically regulate all the parameters needed for tracking, which makes it flexible and easily usable for different applications

Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome associated with COVID-19: An Emulated Target Trial Analysis.

RATIONALE: Whether COVID patients may benefit from extracorporeal membrane oxygenation (ECMO) compared with conventional invasive mechanical ventilation (IMV) remains unknown. OBJECTIVES: To estimate the effect of ECMO on 90-Day mortality vs IMV only Methods: Among 4,244 critically ill adult patients with COVID-19 included in a multicenter cohort study, we emulated a target trial comparing the treatment strategies of initiating ECMO vs. no ECMO within 7 days of IMV in patients with severe acute respiratory distress syndrome (PaO2/FiO2 <80 or PaCO2 ≥60 mmHg). We controlled for confounding using a multivariable Cox model based on predefined variables. MAIN RESULTS: 1,235 patients met the full eligibility criteria for the emulated trial, among whom 164 patients initiated ECMO. The ECMO strategy had a higher survival probability at Day-7 from the onset of eligibility criteria (87% vs 83%, risk difference: 4%, 95% CI 0;9%) which decreased during follow-up (survival at Day-90: 63% vs 65%, risk difference: -2%, 95% CI -10;5%). However, ECMO was associated with higher survival when performed in high-volume ECMO centers or in regions where a specific ECMO network organization was set up to handle high demand, and when initiated within the first 4 days of MV and in profoundly hypoxemic patients. CONCLUSIONS: In an emulated trial based on a nationwide COVID-19 cohort, we found differential survival over time of an ECMO compared with a no-ECMO strategy. However, ECMO was consistently associated with better outcomes when performed in high-volume centers and in regions with ECMO capacities specifically organized to handle high demand. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Suivi d'objets en imagerie aérienne

This thesis results from a CIFRE collaboration with Thales Optronique, a compnay which develops airborne systems for "laser guided armaments". The operating mode used by these systems requires on behalf of the pilot an important workload. The objective of this thesis is to reduce this responsibility, by making the current trackers more robust to the three dimensional effects, in particular in the presence of reliefs.The main theme of this thesis is the tracking of objects from aerial images. We want to extract some three dimensional information from video sequences in order to improve the existing algorithms. For that purpose, we place ourselves in a Bayesian framework and formulate the tracking problem by means of a particle filter. We set up three algorithms: The first one is based on geometrical models (that can be 2D or 3D), that we combine with a particle filter. We add a supplementary stage to the classical particle filter, allowing us to change model when the algorithm judges it necessary. And the last one combines both precedents; the integration of histograms and contour information in a particle filter allows not only to have a more robust tracker, but also to take into account the 3D real information of the observed scene.An evaluation protocol has been set up to estimate the performances of these algorithms. Results illustrate the performances of these algorithms.Cette thèse résulte d'une collaboration CIFRE avec Thalès Optronique, société qui conçoit et développe des systèmes de désignation aéroportés pour des armements guidés laser. Le mode opératoire utilisé par ces systèmes requiert de la part du pilote une charge de travail importante. L'objectif de cette thèse est d'alléger cette charge, en rendant les systèmes de suivi actuels plus robustes face à des effets de perspective.Le thème principal de cette thèse est donc le suivi d'objets à partir d'images aériennes. Nous souhaitons utiliser la faisabilité d'une extraction d'information 3D à partir de séquences vidéo afin d'améliorer les algorithmes de suivi de matériels aéroportés existants. Pour cela, nous nous plaçons dans un cadre bayésien et formulons le suivi de manière probabiliste, au moyen d'un filtre particulaire. Nous avons mis en place trois algorithmes:Le premier est basé sur des histogrammes de couleurs, que l'on combine à un filtrage particulaire;c'est un suivi purement 2D dans le sens où aucune information 3D réelle de la scène est utilisée.Le second est basé sur des modèles géométriques (qui peuvent être 2D ou 3D), que l'on combine à un filtrage particulaire. Nous ajoutons une étape supplémentaire au filtrage particulaire classique, nous permettant de changer de modèle lorsque l'algorithme le juge nécessaire.Enfin, le dernier algorithme combine les deux précédents; l'intégration d'histogrammes de couleurs et d'informations de contours dans un filtre particulaire permet non seulement de rendre le suivi d'objets plus robuste, mais aussi de prendre en compte de l'information 3D réelle de la scène observée.Un protocole d'évaluation a été mis en place pour évaluer les performances de ces algorithmes. Des résultats illustrent les performances de ces algorithmes

3D Object Tracking Based on Multi-Model Particle Filtering

International audienc

Suivi Adaptatif d'Objets Non Rigides Basé sur des Histogrammes de Couleur et une Sélection Automatique de Paramètres

National audienceOne of the main difficulties in visual tracking is to take into account appearance changes (not only of the target but also of or due to the scene, illumination for example). The use of a Bayesian framework is very flexible and has proven to be very efficient in visual tracking. Moreover, color or greylevel histograms allow to track an objet with a low computational cost. The recently proposed color-based trackers integrated in a probabilistic framework [7, 5] are efficient for a given application (face tracking for example) but can not be generalized easily, due to the initialization and the adjustment of the different tracker parameters that are dependent on the input sequence. This paper presents a method based on color integrated in a particle filter that allows to cope with some of the usual problems of visual tracking (occlusions, target appearance changes, changes in resolution or in illumination) and to adapt easily to different applications (tracking of structures in aerial imagery as well as football players). The novelty of the tracker is its ability to automatically regulate all the parameters needed for tracking, which makes it flexible and easily usable for different applications.Une des principales difficultés du suivi d'objets dans une séquence vidéo réside dans la prise en compte des changements d'apparence (non seulement ceux de la cible, mais aussi ceux qui proviennent de la scène directement, les changements d'illumination par exemple). Le cadre Bayésien est très flexible et ses performances dans le domaine du suivi d'un ou plusieurs objets ont été prouvées. D'autre part, les histogrammes de niveaux de gris ou de couleurs permettent de suivre un objet avec une complexité calculatoire faible. Les algorithmes de suivi d'objetsreposant sur des histogrammes de couleurs intégrés dans un cadre bayésien [5, 7] se sont montrés performants pour une application donnée (le suivi de visage par exemple), mais ne peuvent pas être généralisés facilement, les paramètres et l'initialisation de l'algorithme étant spécifiques à la séquence d'entrée. Cet article présente une méthode basée sur des histogrammes de niveaux de gris combinés à un filtrage particulaire qui permet de résoudre un certain nombre des problèmes traditionnels du suivi d'objets (occultations, changements d'apparence de l'objet, changements d'échelle ou d'illumination de la scène), et de s'adapter facilement à la séquence d'entrée (suivi de joueur de football, de structures dans des images aériennes, de visages). La nouveauté de l'algorithme réside dans sa capacité à fixer automatiquement tous les paramètres nécessaires au suivi

3D Object Tracking Based on Multi-Model Particle Filtering

International audienc

Suivi d'objets en imagerie aérienne

Cette thèse résulte d'une collaboration CIFRE avec Thales Optronique. Le thème principal est le suivi d'objets à partir d'images aériennes. Nous souhaitons utiliser la faisabilité d'une extraction 30 à partir de séquences vidéo afin d'améliorer les algorithmes de suivi de matériels aéroportés existants. Pour cela, nous nous plaçons dans un cadre bayésien et formulons le suivi de manière probabiliste, au moyen d'un filtre particulaire. Nous avons mis en place trois algorithmes: le premier est un suivi 20 basé sur des histogrammes de couleurs. Le second est basé sur des modèles géométriques (20/30) que l'on combine à un filtrage particulaire. Nous avons introduit une matrice de transition permettant à l'algorithme de changer de modèle lorsqu'il le juge nécessaire. Enfin, le dernier algorithme combine les deux précédents. Un protocole d'évaluation a été mis en place pour évaluer les performances de ces algorithmes, et des résultats illustrent ces performancesGRENOBLE1-BU Sciences (384212103) / SudocSudocFranceF

Improvement of Barley yellow dwarf virus-PAV detection in single aphids using a fluorescent real time RT-PCR

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Crystal Structure and Solution NMR Dynamics of a D (Type II) Peroxiredoxin Glutaredoxin and Thioredoxin Dependent: A New Insight into the Peroxiredoxin Oligomerism

International audiencePeroxiredoxins (Prxs) constitute a family of thiol peroxidases that reduce hydrogen peroxide, peroxinitrite, and hydroperoxides using a strictly conserved cysteine. Very abundant in all organisms, Prxs are produced as diverse isoforms characterized by different catalytic mechanisms and various thiol-containing reducing agents. The oligomeric state of Prxs and the link with their functionality is a subject of intensive research. We present here a combined X-ray and nuclear magnetic resonance (NMR) study of a plant Prx that belongs to the D-Prx (type II) subfamily. The Populus trichocarpa Prx is the first Prx shown to be regenerated in vitro by both the glutaredoxin and thioredoxin systems. The crystal structure and solution NMR provide evidence that the reduced protein is a specific noncovalent homodimer both in the crystal and in solution. The dimer interface is roughly perpendicular to the plane of the central sheet and differs from the interface of A- and B-Prx dimers, where proteins associate in the plane parallel to the sheet. The homodimer interface involves residues strongly conserved in the D (type II) Prxs, suggesting that all Prxs of this family can homodimerize. The study provides a new insight into the Prx oligomerism and the basis for protein-protein and enzyme-substrate interaction studies by NMR

Identification and characterization of highly versatile peptide-vectors that bind non-competitively to the low-density lipoprotein receptor for in vivo targeting and delivery of small molecules and protein cargos

International audienceInsufficient membrane penetration of drugs, in particular biotherapeutics and/or low target specificity remain a major drawback in their efficacy. We propose here the rational characterization and optimization of peptides to be developed as vectors that target cells expressing specific receptors involved in endocytosis or transcytosis. Among receptors involved in receptor-mediated transport is the LDL receptor. Screening complex phage-displayed peptide libraries on the human LDLR (hLDLR) stably expressed in cell lines led to the characterization of a family of cyclic and linear peptides that specifically bind the hLDLR. The VH41 1 lead cyclic peptide allowed endocytosis of payloads such as the S-Tag peptide or antibodies into cells expressing the hLDLR. Size reduction and chemical optimization of this lead peptide-vector led to improved receptor affinity. The optimized peptide-vectors were successfully conjugated to cargos of different nature and size including small organic molecules, siRNAs, peptides or a protein moiety such as an Fc fragment. We show that in all cases, the peptide-vectors retain their binding affinity to the hLDLR and potential for endocytosis. Following i.v. administration in wild type or Idlr-!-mice, an Fc fragment chemically conjugated or fused in C-terminal to peptide-vectors showed significant biodistribution in LDLR-enriched organs. We have thus developed highly versatile peptide-vectors endowed with good affinity for the LDLR as a target receptor. These peptide-vectors have the potential to be further developed for efficient transport of therapeutic or imaging agents into cells-including pathological cells-or organs that express the LDLR