34-Issue 3

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https://diglib.eg.org/handle/10.2312/12458

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Browsing 34-Issue 3 by Subject "Computer Graphics [I.3.3]"

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    Dual Adjacency Matrix: Exploring Link Groups in Dense Networks
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Dinkla, Kasper; Henry-Riche, Nathalie; Westenberg, Michel A.; H. Carr, K.-L. Ma, and G. Santucci
    Node grouping is a common way of adding structure and information to networks that aids their interpretation. However, certain networks benefit from the grouping of links instead of nodes. Link communities, for example, are a form of link groups that describe high-quality overlapping node communities. There is a conceptual gap between node groups and link groups that poses an interesting visualization challenge. We introduce the Dual Adjacency Matrix to bridge this gap. This matrix combines node and link group techniques via a generalization that also enables it to be coordinated with a node-link-contour diagram. These methods have been implemented in a prototype that we evaluated with an information scientist and neuroscientist via interviews and prototype walk- throughs. We demonstrate this prototype with the analysis of a trade network and an fMRI correlation network.
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    Rule-Enhanced Transfer Function Generation for Medical Volume Visualization
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Cai, Li-Le; Nguyen, Binh P.; Chui, Chee-Kong; Ong, Sim-Heng; H. Carr, K.-L. Ma, and G. Santucci
    In volume visualization, transfer functions are used to classify the volumetric data and assign optical properties to the voxels. In general, transfer functions are generated in a transfer function space, which is the feature space constructed by data values and properties derived from the data. If volumetric objects have the same or overlapping data values, it would be difficult to separate them in the transfer function space. In this paper, we present a rule-enhanced transfer function design method that allows important structures of the volume to be more effectively separated and highlighted. We define a set of rules based on the local frequency distribution of volume attributes. A rule-selection method based on a genetic algorithm is proposed to learn the set of rules that can distinguish the user-specified target tissue from other tissues. In the rendering stage, voxels satisfying these rules are rendered with higher opacities in order to highlight the target tissue. The proposed method was tested on various volumetric datasets to enhance the visualization of important structures that are difficult to be visualized by traditional transfer function design methods. The results demonstrate the effectiveness of the proposed method.