VMV2021

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https://diglib.eg.org/handle/10.2312/2633087

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Browsing VMV2021 by Subject "Human centered computing"

Now showing 1 - 4 of 4
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    Axes Bundling and Brushing in Star Coordinates
    (The Eurographics Association, 2021) Rave, Hennes; Molchanov, Vladimir; Linsen, Lars; Andres, Bjoern and Campen, Marcel and Sedlmair, Michael
    Visual analysis of multidimensional data commonly involves dimensionality reduction to project the data samples into a lowerdimensional visual space. Star coordinates (SC) provide a means to explore the multidimensional data distribution by interactively changing the linear projection matrix. While SC have the advantages of being intuitive, allowing for relating the data samples to their original dimensions, having low computation costs, and scaling well with the number of data samples, they have the disadvantages of not scaling well to larger number of dimensions and being restricted to linear projections. We address these short-comings by introducing novel SC interactions. First, interactive bundling of axes is proposed to reduce the number of dimensions. While bundles are fully customizable, the bundling interactions are supported by visualizations of correlation matrices and hierarchical axes clustering dendrograms. Second, we enhance classical region brushing in SC projections with axes brushing, which allows for multidimensional cluster selection, even if two (separable) clusters are projected to the same area of the visible space. Axes brushing is supported by visualizing 1D histograms of data distributions along the SC axes. Our brushing interactions alleviate the restriction of SC to linear projections. The integration of histograms into SC also eases other interactions such as moving axes to change the projection matrix. A user study evaluates how analysis tasks for labeled and unlabeled multidimensional data can benefit from our extensions.
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    CoSi: Visual Comparison of Similarities in High-Dimensional Data Ensembles
    (The Eurographics Association, 2021) Heim, Anja; Gröller, Eduard; Heinzl, Christoph; Andres, Bjoern and Campen, Marcel and Sedlmair, Michael
    Comparative analysis of multivariate datasets, e.g. of advanced materials regarding the characteristics of internal structures (fibers, pores, etc.), is of crucial importance in various scientific disciplines. Currently domain experts in materials science mostly rely on sequential comparison of data using juxtaposition. Our work assists domain experts to perform detailed comparative analyses of large ensemble data in materials science applications. For this purpose, we developed a comparative visualization framework, that includes a tabular overview and three detailed visualization techniques to provide a holistic view on the similarities in the ensemble. We demonstrate the applicability of our framework on two specific usage scenarios and verify its techniques using a qualitative user study with 12 material experts. The insights gained from our work represent a significant advancement in the field of comparative material analysis of high-dimensional data. Our framework provides experts with a novel perspective on the data and eliminates the need for time-consuming sequential exploration of numerical data.
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    EMCA: Explorer of Monte Carlo based Algorithms
    (The Eurographics Association, 2021) Ruppert, Lukas; Kreisl, Christoph; Blank, Nils; Herholz, Sebastian; Lensch, Hendrik P. A.; Andres, Bjoern and Campen, Marcel and Sedlmair, Michael
    Debugging or analyzing the performance of global illumination algorithms is a challenging task due to the complex path-scene interaction and numerous places where errors and programming bugs can occur. We present a novel, lightweight visualization tool to aid in the understanding of global illumination and the debugging of rendering frameworks. The tool provides detailed information about intersections and light transport paths. Users can add arbitrary data of their choosing to each intersection, based on their specific demands. Aggregate plots allow users to quickly discover and select outliers for further inspection across the globally linked visualization views. That information is further coupled with 3D visualization of the scene where additional aggregated information on the surfaces can be inspected in false colors. These include 3D heat maps such as the density of intersections as well as more advanced colorings such as a diffuse transport approximation computed from local irradiance samples and diffuse material approximations. The necessary data for the 3D coloring is collected as a side-product of quickly rendering the image at low sample counts without significantly slowing down the rendering process. It requires almost no precomputation and very little storage compared to point cloud-based approaches. We present several use cases of how novices and advanced rendering researchers can leverage the presented tool to speed up their research.
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    Visualizing Temporal-Thematic Patterns in Text Collections
    (The Eurographics Association, 2021) Knabben, Moritz; Baumann, Martin; Blascheck, Tanja; Ertl, Thomas; Koch, Steffen; Andres, Bjoern and Campen, Marcel and Sedlmair, Michael
    Visualizing the temporal evolution of texts is relevant for many domains that seek to gain insight from text repositories. However, existing visualization methods for text collections do not show fine-grained temporal-thematic patterns. Therefore, we developed and analyzed a new visualization method that aims at uncovering such patterns. Specifically, we project texts to one dimension, which allows positioning texts in a 2D diagram of projection space and time. For projection, we employed two manifold learning algorithms: the self-organizing map (SOM) and UMAP. To assess the utility of our method, we experimented with real-world datasets and discuss the resulting visualizations. We find our method facilitates relating patterns and extracting associated texts beyond what is possible with previous techniques. We also conducted interviews with historians to show that our prototypical system supports domain experts in their analysis tasks.