EuroVisShort2012

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https://diglib.eg.org/handle/10.2312/988

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Browsing EuroVisShort2012 by Subject "Categories and Subject Descriptors (according to ACM CCS): I.3.8 [Computer Graphics]: Applications"

Now showing 1 - 2 of 2
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    CrystalExplorer: An Interactive Knowledge-Assisted System for Visual Design of Solar Cell Crystal Structures
    (The Eurographics Association, 2012) Aboulhassan, Amal; Li, Ruipeng; Knox, Christopher; Amassian, Aram; Hadwiger, Markus; Miriah Meyer and Tino Weinkaufs
    Crystallography is a key research tool in materials science. The chemical properties of materials are often controlled by the geometric properties of crystals. Accordingly, visualizing the 3D structure of crystals is an important task in materials exploration. The current crystallography visualization systems are limited by focusing on the visualization of pre-defined crystal structures, and a lack of capabilities for editing and exploring different variations and levels of abstraction. To remove this limitation, we propose a new paradigm for knowledge-assisted visual exploration of crystals where the user can use semantic rules to define clusters of atoms with certain geometric properties. To test the usefulness of this system, we have applied it for the design of materials for solar cells. Using our proposed system, materials scientists can interactively create and visualize structures of interest inside the crystals in a relatively short time. This could not be achieved using their previous visualization workflow.
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    Pattern Visualization of Human Connectome Data
    (The Eurographics Association, 2012) Guo, Yishi; Wang, Yang; Fang, Shiaofen; Chao, Hongyang; Saykin, Andrew; Shen, Li; Miriah Meyer and Tino Weinkaufs
    The human brain is a complex network with countless connected neurons, and can be described as a "connectome". Existing studies on analyzing human connectome data are primarily focused on characterizing the brain networks with a small number of easily computable measures that may be inadequate for revealing complex relationship between brain function and its structural substrate. To facilitate large-scale connectomic analysis, in this paper, we propose a powerful and flexible volume rendering scheme to effectively visualize and interactively explore thousands of network measures in the context of brain anatomy, and to aid pattern discovery.We demonstrate the effectiveness of the proposed scheme by applying it to a real connectome data set.