VMV18

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https://diglib.eg.org/handle/10.2312/2632666

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Browsing VMV18 by Subject "Information visualization"

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    Automatic Generation of Saliency-based Areas of Interest for the Visualization and Analysis of Eye-tracking Data
    (The Eurographics Association, 2018) Fuhl, Wolfgang; Kuebler, Thomas; Santini, Thiago; Kasneci, Enkelejda; Beck, Fabian and Dachsbacher, Carsten and Sadlo, Filip
    Areas of interest (AOIs) are a powerful basis for the analysis and visualization of eye-tracking data. They allow to relate eyetracking metrics to semantic stimulus regions and to perform further statistics. In this work, we propose a novel method for the automated generation of AOIs based on saliency maps. In contrast to existing methods from the state-of-the-art, which generate AOIs based on eye-tracking data, our method generates AOIs based solely on the stimulus saliency, mimicking thus our natural vision. This way, our method is not only independent of the eye-tracking data, but allows to work AOI-based even for complex stimuli, such as abstract art, where proper manual definition of AOIs is not trivial. For evaluation, we cross-validate support vector machine classifiers with the task of separating visual scanpaths of art experts from those of novices. The motivation for this evaluation is to use AOIs as projection functions and to evaluate their robustness on different feature spaces. A good AOI separation should result in different feature sets that enable a fast evaluation with a widely automated work-flow. The proposed method together with the data shown in this paper is available as part of the software EyeTrace [?] http://www.ti.unituebingen. de/Eyetrace.1751.0.html.
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    Clustering for Stacked Edge Splatting
    (The Eurographics Association, 2018) Abdelaal, Moataz; Hlawatsch, Marcel; Burch, Michael; Weiskopf, Daniel; Beck, Fabian and Dachsbacher, Carsten and Sadlo, Filip
    We present a time-scalable approach for visualizing dynamic graphs. By adopting bipartite graph layouts known from parallel edge splatting, individual graphs are horizontally stacked by drawing partial edges, leading to stacked edge splatting. This allows us to uncover the temporal patterns together with achieving time-scalability. To preserve the graph structural information, we introduce the representative graph where edges are aggregated and drawn at full length. The representative graph is then placed on the top of the last graph in the (sub)sequence. This allows us to obtain detailed information about the partial edges by tracing them back to the representative graph. We apply sequential temporal clustering to obtain an overview of different temporal phases of the graph sequence together with the corresponding structure for each phase. We demonstrate the effectiveness of our approach by using real-world datasets.