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6 results for “tessellation”
Accompanying dataset for the paper "Mesh Density and Geodesic Tortuosity in Planar Triangular Tesselations Devoted to Fracture Mechanics"
<h2>Contributions</h2> <ul> <li>Author #1 is the major contributor to the paper writing. Author #1 has created most of the Figures in the paper and has provided all the results concerning the geodesic tortuosity of planar triangular meshes.</li> <li>Author #2 and Author #3 have provided the results concerning the density of planar triangular meshes.</li> </ul> <h2>Funding sources</h2> <ul> <li>This work was funded by the French Institute for Radiation Protection and Nuclear Safety (IRSN) and the University of Montpellier, France.</li> </ul> <h2>Data structure and information</h2> <p>All the results and figures presented in the paper have been obtained with a python script available in the <code>workflows</code> folder. </p> <p>Data files processed by that script are provided in the <code>data</code> folder. The pickle files <code>t_{nb}.pkl</code> inside the <code>data/pkl</code> folder contain lists of tortuosity values computed on a <code>gmsh</code> mesh for several paths containing various number of edges (<code>nb</code>).</p> <p><code>pkl</code>files are produced via a function call to <code>main.py:comp_real_tortuosities(nb)</code></p> <h2>Data structure and information</h2> <ul> <li><code>workflows/</code> - folder containing plotting scripts<ul> <li><code>reproduce.sh</code> - bash script launching python and compressing figures</li> <li><code>main.py</code> - python script for figure creation.</li> <li><code>/utils</code> - folder containing additional python scripts for figures creation</li> <li><code>/figures</code> - folder containing the figures produced by the python script</li> </ul> </li> <li><code>data/</code> - data folder<ul> <li><code>gmsh</code> - folder containing mesh in gmsh information</li> <li><code>pkl</code> - folder containing the pickled vectors "t_{nb}.pkl".</li> </ul> </li> </ul> <h2>Paper Description</h2> <p>In fracture mechanics, the mesh sensitivity is a key issue. It is particularly true concerning cohesive volumetric finite element methods in which the crack path and the overall behavior are respectively influenced by the mesh topology and the mesh density. Poisson-Delaunay tessellations parameters, including the edge length distributions, were widely studied in the literature but very few works concern the mesh density and topology in Delaunay type meshes suitable for finite element simulations, which is of crucial interest for practical use.Starting from previous results concerning Poisson-Delaunay tessellations and studying in detail the Lloyd relaxation algorithm, we propose estimates for the probability density functions of the edge length and triangle top angles sets. These estimates depend both on the intensity of the underlying point process and on an efficiency index associated to the global quality of the mesh. The global and local accuracies of these estimates are checked for various standard mesh generators. Finally the mesh density and geodesic tortuosity are estimated for standard random or structured triangular meshes typically used in finite element simulations.These results provide practical formulas to estimate bias introduced by the mesh density and topology onthe results of cohesive-volumetric finite element simulations.</p>
Data from: Automated segmentation of complex patterns in biological tissues: lessons from stingray tessellated cartilage
Introduction - Many biological structures show recurring tiling patterns on one structural level or the other. Current image acquisition techniques are able to resolve those tiling patterns to allow quantitative analyses. The resulting image data, however, may contain an enormous number of elements. This renders manual image analysis infeasible, in particular when statistical analysis is to be conducted, requiring a larger number of image data to be analyzed. As a consequence, the analysis process needs to be automated to a large degree. In this paper, we describe a multi-step image segmentation pipeline for the automated segmentation of the calcified cartilage into individual tesserae from computed tomography images of skeletal elements of stingrays. Methods - Besides applying state-of-the-art algorithms like anisotropic diffusion smoothing, local thresholding for foreground segmentation, distance map calculation, and hierarchical watershed, we exploit a graph-based representation for fast correction of the segmentation. In addition, we propose a new distance map that is computed only in the plane that locally best approximates the calcified cartilage. This distance map drastically improves the separation of individual tesserae. We apply our segmentation pipeline to hyomandibulae from three individuals of the round stingray (Urobatis halleri), varying both in age and size. Results - Each of the hyomandibula datasets contains approximately 3000 tesserae. To evaluate the quality of the automated segmentation, four expert users manually generated ground truth segmentations of small parts of one hyomandibula. These ground truth segmentations allowed us to compare the segmentation quality w.r.t. individual tesserae. Additionally, to investigate the segmentation quality of whole skeletal elements, landmarks were manually placed on all tesserae and their positions were then compared to the segmented tesserae. With the proposed segmentation pipeline, we sped up the processing of a single skeletal element from days or weeks to a few hours.
Data from: Concordant genetic breaks, identified by combining clustering and tessellation methods, in two co-distributed alpine plant species
Open the record for dataset details and reuse information.
Data from: Automated segmentation of complex patterns in biological tissues: lessons from stingray tessellated cartilage
Open the record for dataset details and reuse information.
A Tessellated Lymphoid Network Provides Whole-Body Antigen Surveillance in Zebrafish
GEO Series GSE215189. Danio rerio. 2 samples. Type: Expression profiling by high throughput sequencing.
Tessellator version 01
Source: Objaverse 1.0 / Sketchfab
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