EuroVA17

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https://diglib.eg.org/handle/10.2312/2631436

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Browsing EuroVA17 by Subject "centered computing"

Now showing 1 - 4 of 4
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    Combining Cluster and Outlier Analysis with Visual Analytics
    (The Eurographics Association, 2017) Bernard, Jürgen; Dobermann, Eduard; Sedlmair, Michael; Fellner, Dieter W.; Michael Sedlmair and Christian Tominski
    Cluster and outlier analysis are two important tasks. Due to their nature these tasks seem to be opposed to each other, i.e., data objects either belong to a cluster structure or a sparsely populated outlier region. In this work, we present a visual analytics tool that allows the combined analysis of clusters and outliers. Users can add multiple clustering and outlier analysis algorithms, compare results visually, and combine the algorithms' results. The usefulness of the combined analysis is demonstrated using the example of labeling unknown data sets. The usage scenario also shows that identified clusters and outliers can share joint areas of the data space.
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    Visual Analysis of Geo-spatial Data in 3D Terrain Environments using Focus+Context
    (The Eurographics Association, 2017) Richter, Christian; Dübel, Steve; Schumann, Heidrun; Michael Sedlmair and Christian Tominski
    Visual analysis of geo-spatial data represented within a three-dimensional frame of reference is a challenging task. Focus+ Context is a common concept that aids this process. This paper addresses the question, how Focus+Context can be applied to a visualization of multivariate weather data along with 3D terrain data. For this purpose, the focus can be specified with regard to both, the terrain and the weather data, utilizing different strategies. Based on the specified focus, the associated context is derived automatically. Data within focus is emphasized, whereas context information is shown with less detail. To this end, various rendering strategies are proposed. We demonstrate our approach by several examples that were generated by the Focus+Context functionality of our visual analytics tool TedaVis.
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    Visual Analysis of Optical Coherence Tomography Data in Ophthalmology
    (The Eurographics Association, 2017) Röhlig, Martin; Rosenthal, Paul; Schmidt, Christoph; Schumann, Heidrun; Stachs, Oliver; Michael Sedlmair and Christian Tominski
    Optical coherence tomography (OCT) enables noninvasive high-resolution 3D imaging of the human retina and thus, plays a fundamental role in detecting a wide range of ocular diseases. Despite OCT's diagnostic value, managing and analyzing resulting data is challenging. We apply two visual analytics strategies for supporting retinal assessment in practice. First, we provide an interface for unifying and structuring data from different sources into a common basis. Fusing that basis with medical records and augmenting it with analytically derived information facilitates thorough investigations. Second, we present a tailored visual analysis tool for presenting, selecting, and emphasizing different aspects of the attributed data. This enables free exploration, reducing the data to relevant subsets, and focusing on details. By applying both strategies, we effectively enhance the management and the analysis of OCT data for assisting medical diagnoses.
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    Visual Analytics for Multitemporal Aerial Image Georeferencing
    (The Eurographics Association, 2017) Amor-Amorós, Albert; Federico, Paolo; Miksch, Silvia; Zambanini, Sebastian; Brenner, Simon; Sablatnig, Robert; Michael Sedlmair and Christian Tominski
    Georeferencing of multitemporal aerial imagery is a time-consuming and challenging task that typically requires a high degree of human intervention, and which appears in application domains of critical importance, like unexploded ordnance detection. In order to make a semi-automatic scenario possible, we introduce a Visual Analytics approach for multitemporal aerial image georeferencing designed in close collaboration with real-world analysts that face the problem on a daily basis, and implemented by combining computer vision and interactive visual exploration methods. We report on informal validation findings resulting from the integration of our solution into our users' GIS platform of choice, which positively illustrate its effectiveness and time-saving potential.