Search results for "Mesh"

Reliable numerical solution of a class of nonlinear elliptic problems generated by the Poisson-Boltzmann equation

2020

We consider a class of nonlinear elliptic problems associated with models in biophysics, which are described by the Poisson-Boltzmann equation (PBE). We prove mathematical correctness of the problem, study a suitable class of approximations, and deduce guaranteed and fully computable bounds of approximation errors. The latter goal is achieved by means of the approach suggested in [S. Repin, A posteriori error estimation for variational problems with uniformly convex functionals. Math. Comp., 69:481-500, 2000] for convex variational problems. Moreover, we establish the error identity, which defines the error measure natural for the considered class of problems and show that it yields computa…

Diagnostic des virus des gastroentérites aiguës de l'adulte dans les pays en voie de développement:intérêt et stratégie.

2009

Modellazione 3D di problemi diffusivi in mezzi anisotropi ed eterogenei

2014

UN’EFFICIENTE METODOLOGIA PER LA SOLUZIONE DEI PROBLEMI DI TRASPORTO ANISOTROPI ETEROGENEI

2012

AN EFFICIENT SOLUTION OF HETEROGENEOUS ANISOTROPIC CONVECTION/DIFFUSION TRANSPORT PROBLEMS

2012

EFFICIENT SOLUTION OF HETEROGENEOUS ANISOTROPIC DIFFUSION PROBLEMS

2012

Autophagy

2012

Klionsky, Daniel J. et al.

SHREC'12 Track: 3D mesh segmentation

2012

International audience; 3D mesh segmentation is a fundamental process in many applications such as shape retrieval, compression, deformation, etc. The objective of this track is to evaluate the performance of recent segmentation methods using a ground-truth corpus and an accurate similarity metric. The ground-truth corpus is composed of 28 watertight models, grouped in five classes (animal, furniture, hand, human and bust) and each associated with 4 ground-truth segmentations done by human subjects. 3 research groups have participated to this track, the accuracy of their segmentation algorithms have been evaluated and compared with 4 other state-of-the-art methods.

Combination Of Handcrafted And Deep Learning-Based Features For 3d Mesh Quality Assessment

2020

We propose in this paper a novel objective method to evaluate the perceived visual quality of 3D meshes. The proposed method in no-reference, it relies only on the distorted mesh for the quality estimation. It is based on a pre-trained convolutional neural network (i.e VGG to extract features from the distorted mesh) and handcrafted features extracted directly from the 3D mesh (i.e curvature and dihedral angle). A General Regression Neural Network (GRNN) is used to learn the statistical parameters of the feature vectors and estimate the quality score. Experimental results from for subjective databases (LIRIS masking, LIRIS/EPFL generalpurpose, UWB compression and LEETA simplification) and c…

Full Reference Mesh Visual Quality Assessment Using Pre-Trained Deep Network and Quality Indices

2019

In this paper, we propose an objective quality metric to evaluate the perceived visual quality of 3D meshes. Our method relies on pre-trained convolutional neural network i.e VGG to extract features from the distorted mesh and its reference. Quality indices from well-known mesh visual quality metrics are concatenated with the extracted features resulting a global feature vector. this latter is used to learn the support vector regression (SVR) to predict the final quality score. Experimental results from two subjective databases (LIRIS masking database and LIRIS/EPFL general-purpose database) and comparisons with seven objective metrics cited in the state-of-the-art demonstrate the effective…