Italian Chapter Conference 2019 - Smart Tools and Apps in computer Graphics

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2632820

Browse

Browsing Italian Chapter Conference 2019 - Smart Tools and Apps in computer Graphics by Subject "Applied computing"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Immersive Environment for Creating, Proofreading, and Exploring Skeletons of Nanometric Scale Neural Structures
    (The Eurographics Association, 2019) Boges, Daniya; Calì, Corrado; Magistretti, Pierre J.; Hadwiger, Markus; Sicat, Ronell; Agus, Marco; Agus, Marco and Corsini, Massimiliano and Pintus, Ruggero
    We present a novel immersive environment for the exploratory analysis of nanoscale cellular reconstructions of rodent brain samples acquired through electron microscopy. The system is focused on medial axis representations (skeletons) of branched and tubular structures of brain cells, and it is specifically designed for: i) effective semi-automatic creation of skeletons from surface-based representations of cells and structures, ii) fast proofreading, i.e., correcting and editing of semi-automatically constructed skeleton representations, and iii) useful exploration, i.e., measuring, comparing, and analyzing geometric features related to cellular structures based on medial axis representations. The application runs in a standard PC-tethered virtual reality (VR) setup with a head mounted display (HMD), controllers, and tracking sensors. The system is currently used by neuroscientists for performing morphology studies on sparse reconstructions of glial cells and neurons extracted from a sample of the somatosensory cortex of a juvenile rat.
  • No Thumbnail Available
    Item
    Visual Representation of Region Transitions in Multi-dimensional Parameter Spaces
    (The Eurographics Association, 2019) Fernandes, Oliver; Frey, Steffen; Reina, Guido; Ertl, Thomas; Agus, Marco and Corsini, Massimiliano and Pintus, Ruggero
    We propose a novel visual representation of transitions between homogeneous regions in multi-dimensional parameter space. While our approach is generally applicable for the analysis of arbitrary continuous parameter spaces, we particularly focus on scientific applications, like physical variables in simulation ensembles. To generate our representation, we use unsupervised learning to cluster the ensemble members according to their mutual similarity. In doing this, clusters are sorted such that similar clusters are located next to each other. We then further partition the clusters into connected regions with respect to their location in parameter space. In the visualization, the resulting regions are represented as glyphs in a matrix, indicating parameter changes which induce a transition to another region. To unambiguously associate a change of data characteristics to a single parameter, we specifically isolate changes by dimension. With this, our representation provides an intuitive visualization of the parameter transitions that influence the outcome of the underlying simulation or measurement. We demonstrate the generality and utility of our approach on diverse types of data, namely simulations from the field of computational fluid dynamics and thermodynamics, as well as an ensemble of raycasting performance data.