Etude de caractéristiques saillantes sur des maillages 3D par estimation des normales et des courbures discrètes

With the aim to improve and automate the object reproduction chainfrom acquisition to 3D printing .We sought to characterize the salience on 3D objectsmodeled by a 3D mesh structure. For this, we have a state of the art of estimatingdifferential properties methods, namely normal and curvature on discrete surfaces inthe form of 3D mesh. To compare the behavior of different methods, we took a set ofclassic benchmarks in the domain, which are : accuracy, convergence and robustnesswith respect to variations of the neighbourhood. For this, we have established atest protocol emphasizing these qualities. From this first comparision, it was foundthat all the existing methods have shortcomings as these criteria. In order to havean estimation of the differential properties more reliable and accurate we developedtwo new estimators.Dans l'objectif d'améliorer et d'automatiser la chaîne de reproductiond'objet qui va de l'acquisition à l'impression 3D. Nous avons cherché à caractériserde la saillance sur les objets 3D modélisés par la structure d'un maillage 3D.Pour cela, nous avons fait un état de l'art des méthodes d'estimation des proprié-tés différentielles, à savoir la normale et la courbure, sur des surfaces discrètes sousla forme de maillage 3D. Pour comparer le comportement des différentes méthodes,nous avons repris un ensemble de critères de comparaison classique dans le domaine,qui sont : la précision, la convergence et la robustesse par rapport aux variations duvoisinage. Pour cela, nous avons établi un protocole de tests mettant en avant cesqualités. De cette première comparaison, il est ressorti que l'ensemble des méthodesexistantes présentent des défauts selon ces différents critères. Afin d'avoir une estimationdes propriétés différentielles plus fiable et précise nous avons élaboré deuxnouveaux estimateurs

APP-RUSS: Automated Path Planning for Robotic Ultrasound System

Autonomous robotic ultrasound System (RUSS) has been extensively studied. However, fully automated ultrasound image acquisition is still challenging, partly due to the lack of study in combining two phases of path planning: guiding the ultrasound probe to the scan target and covering the scan surface or volume. This paper presents a system of Automated Path Planning for RUSS (APP-RUSS). Our focus is on the first phase of automation, which emphasizes directing the ultrasound probe's path toward the target over extended distances. Specifically, our APP-RUSS system consists of a RealSense D405 RGB-D camera that is employed for visual guidance of the UR5e robotic arm and a cubic Bezier curve path planning model that is customized for delivering the probe to the recognized target. APP-RUSS can contribute to understanding the integration of the two phases of path planning in robotic ultrasound imaging, paving the way for its clinical adoption

ZerNet: Convolutional Neural Networks on Arbitrary Surfaces via Zernike Local Tangent Space Estimation

In this paper, we propose a novel formulation to extend CNNs to two-dimensional (2D) manifolds using orthogonal basis functions, called Zernike polynomials. In many areas, geometric features play a key role in understanding scientific phenomena. Thus, an ability to codify geometric features into a mathematical quantity can be critical. Recently, convolutional neural networks (CNNs) have demonstrated the promising capability of extracting and codifying features from visual information. However, the progress has been concentrated in computer vision applications where there exists an inherent grid-like structure. In contrast, many geometry processing problems are defined on curved surfaces, and the generalization of CNNs is not quite trivial. The difficulties are rooted in the lack of key ingredients such as the canonical grid-like representation, the notion of consistent orientation, and a compatible local topology across the domain. In this paper, we prove that the convolution of two functions can be represented as a simple dot product between Zernike polynomial coefficients; and the rotation of a convolution kernel is essentially a set of 2-by-2 rotation matrices applied to the coefficients. As such, the key contribution of this work resides in a concise but rigorous mathematical generalization of the CNN building blocks

Study of salient features on 3D meshes through discrete normal and curvature estimation.

Dans l'objectif d'améliorer et d'automatiser la chaîne de reproductiond'objet qui va de l'acquisition à l'impression 3D. Nous avons cherché à caractériserde la saillance sur les objets 3D modélisés par la structure d'un maillage 3D.Pour cela, nous avons fait un état de l'art des méthodes d'estimation des proprié-tés différentielles, à savoir la normale et la courbure, sur des surfaces discrètes sousla forme de maillage 3D. Pour comparer le comportement des différentes méthodes,nous avons repris un ensemble de critères de comparaison classique dans le domaine,qui sont : la précision, la convergence et la robustesse par rapport aux variations duvoisinage. Pour cela, nous avons établi un protocole de tests mettant en avant cesqualités. De cette première comparaison, il est ressorti que l'ensemble des méthodesexistantes présentent des défauts selon ces différents critères. Afin d'avoir une estimationdes propriétés différentielles plus fiable et précise nous avons élaboré deuxnouveaux estimateurs.With the aim to improve and automate the object reproduction chainfrom acquisition to 3D printing .We sought to characterize the salience on 3D objectsmodeled by a 3D mesh structure. For this, we have a state of the art of estimatingdifferential properties methods, namely normal and curvature on discrete surfaces inthe form of 3D mesh. To compare the behavior of different methods, we took a set ofclassic benchmarks in the domain, which are : accuracy, convergence and robustnesswith respect to variations of the neighbourhood. For this, we have established atest protocol emphasizing these qualities. From this first comparision, it was foundthat all the existing methods have shortcomings as these criteria. In order to havean estimation of the differential properties more reliable and accurate we developedtwo new estimators

Study of salient features on 3D meshes through discrete normal and curvature estimation.

Dans l'objectif d'améliorer et d'automatiser la chaîne de reproductiond'objet qui va de l'acquisition à l'impression 3D. Nous avons cherché à caractériserde la saillance sur les objets 3D modélisés par la structure d'un maillage 3D.Pour cela, nous avons fait un état de l'art des méthodes d'estimation des proprié-tés différentielles, à savoir la normale et la courbure, sur des surfaces discrètes sousla forme de maillage 3D. Pour comparer le comportement des différentes méthodes,nous avons repris un ensemble de critères de comparaison classique dans le domaine,qui sont : la précision, la convergence et la robustesse par rapport aux variations duvoisinage. Pour cela, nous avons établi un protocole de tests mettant en avant cesqualités. De cette première comparaison, il est ressorti que l'ensemble des méthodesexistantes présentent des défauts selon ces différents critères. Afin d'avoir une estimationdes propriétés différentielles plus fiable et précise nous avons élaboré deuxnouveaux estimateurs.With the aim to improve and automate the object reproduction chainfrom acquisition to 3D printing .We sought to characterize the salience on 3D objectsmodeled by a 3D mesh structure. For this, we have a state of the art of estimatingdifferential properties methods, namely normal and curvature on discrete surfaces inthe form of 3D mesh. To compare the behavior of different methods, we took a set ofclassic benchmarks in the domain, which are : accuracy, convergence and robustnesswith respect to variations of the neighbourhood. For this, we have established atest protocol emphasizing these qualities. From this first comparision, it was foundthat all the existing methods have shortcomings as these criteria. In order to havean estimation of the differential properties more reliable and accurate we developedtwo new estimators

Dlk1-Dio3 cluster miRNAs regulate mitochondrial functions in Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a severe muscle disease caused by impaired expression of dystrophin. Whereas mitochondrial dysfunction is thought to play an important role in DMD, the mechanism of this dysfunction remains to be clarified. Here we demonstrate that in DMD and other muscular dystrophies, a large number of Dlk1-Dio3 clustered miRNAs (DD-miRNAs) are coordinately up-regulated in regenerating myofibers and in the serum. To characterize the biological effect of this dysregulation, 14 DD-miRNAs were simultaneously overexpressed in vivo in mouse muscle. Transcriptomic analysis revealed highly similar changes between the muscle ectopically overexpressing 14 DD-miRNAs and the mdx diaphragm, with naturally up-regulated DD-miRNAs. Among the commonly dysregulated pathway we found repressed mitochondrial metabolism, and oxidative phosphorylation (OxPhos) in particular. Knocking down the DD-miRNAs in iPS-derived skeletal myotubes resulted in increased OxPhos activities. The data suggest that (1) DD-miRNAs are important mediators of dystrophic changes in DMD muscle, (2) mitochondrial metabolism and OxPhos in particular are targeted in DMD by coordinately up-regulated DD-miRNAs. These findings provide insight into the mechanism of mitochondrial dysfunction in muscular dystrophy