EuroVisPosters2017

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https://diglib.eg.org/handle/10.2312/2631437

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Browsing EuroVisPosters2017 by Subject "centered computing"

Now showing 1 - 3 of 3
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    ConTraffic Visual Analytics in Support to Customs Risk-Analysis
    (The Eurographics Association, 2017) Poulymenopoulou, Mikaela; Tsois, Aris; Anna Puig Puig and Tobias Isenberg
    Customs risk analysis is crucial for detecting fraud and contraband goods in the massive flows of internationally traded goods. Most of non-bulk goods are transported in shipping containers and, as customs can control only about 2% of them, efficient customs risk analysis is crucial. In support to EU customs, the Joint Research Centre of the European Commission has developed the ConTraffic visual analytics research prototype. This paper presents the main architectural elements of the application and some visualization and user-interaction techniques selected to enable the route-based risk analysis of large number of shipping containers.
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    Interactive Lens for Effective Time-Series Animation
    (The Eurographics Association, 2017) Chen, Yang; Yang, Jing; Zhao, Ye; Anna Puig Puig and Tobias Isenberg
    We present a lens-based approach to explore animated data visualizations that involve time-series. The core of the approach lies in a novel combination of Lagrangian and Eulerian lenses, which allows us to leverage their complementary advantages to analyze data elements in complex spatial-temporal space. Film art techniques are employed to advance the way shifting the elements across different time and spaces. We illustrate the approach using animated bubble charts.
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    Visually Analyzing Parameter Influence on Optical Coherence Tomography Data in Ophthalmology
    (The Eurographics Association, 2017) Röhlig, Martin; Luboschik, Martin; Prakasam, Ruby Kala; Stachs, Oliver; Schumann, Heidrun; Anna Puig Puig and Tobias Isenberg
    Optical coherence tomography (OCT) enables noninvasive high-resolution imaging of the human retina and therefore, plays a fundamental role in detecting a wide range of ocular diseases. Yet, OCT data often vary in quality and show strong parameter dependencies. We propose a visual analysis approach to support users in understanding the influence of parameters on different aspects of the data. First, we outline the problem scope and derive requirements for a visual parameter analysis of OCT data. Second, we devise matched visual designs that disclose the impact of specific parameter values and the relationships between multiple parameter settings. With our systematic approach we aim at helping users in choosing suitable parameter settings and finding a balance between acquisition effort and data quality.