VMV18

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

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Browsing VMV18 by Subject "centered computing"

Now showing 1 - 5 of 5
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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.
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    Dynamic Environment Mapping for Augmented Reality Applications on Mobile Devices
    (The Eurographics Association, 2018) Monroy, Rafael; Hudon, Matis; Smolic, Aljosa; Beck, Fabian and Dachsbacher, Carsten and Sadlo, Filip
    Augmented Reality is a topic of foremost interest nowadays. Its main goal is to seamlessly blend virtual content in real-world scenes. Due to the lack of computational power in mobile devices, rendering a virtual object with high-quality, coherent appearance and in real-time, remains an area of active research. In this work, we present a novel pipeline that allows for coupled environment acquisition and virtual object rendering on a mobile device equipped with a depth sensor. While keeping human interaction to a minimum, our system can scan a real scene and project it onto a two-dimensional environment map containing RGB+Depth data. Furthermore, we define a set of criteria that allows for an adaptive update of the environment map to account for dynamic changes in the scene. Then, under the assumption of diffuse surfaces and distant illumination, our method exploits an analytic expression for the irradiance in terms of spherical harmonic coefficients, which leads to a very efficient rendering algorithm. We show that all the processes in our pipeline can be executed while maintaining an average frame rate of 31Hz on a mobile device.
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    Identifying Similar Eye Movement Patterns with t-SNE
    (The Eurographics Association, 2018) Burch, Michael; Beck, Fabian and Dachsbacher, Carsten and Sadlo, Filip
    In this paper we describe an approach based on the t-distributed stochastic neighbor embedding (t-SNE) focusing on projecting high-dimensional eye movement data to two dimensions. The lower-dimensional data is then represented as scatterplots reflecting the local structure of the high-dimensional eye movement data and hence, providing a strategy to identify similar eye movement patterns. The scatterplots can be used as means to interact with and to further annotate and analyze the data for additional properties focusing on space, time, or participants. Since t-SNE oftentimes produces groups of data points mapped to and overplotted in small scatterplot regions, we additionally support the modification of data point groups by a force-directed placement as a post processing in addition to t-SNE that can be run after the initial t-SNE algorithm is stopped. This spatial modification can be applied to each identified data point group independently which is difficult to integrate into a standard t-SNE approach. We illustrate the usefulness of our technique by applying it to formerly conducted eye tracking studies investigating the readability of public transport maps and map annotations.
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    The Parallel Eigenvectors Operator
    (The Eurographics Association, 2018) Oster, Timo; Rössl, Christian; Theisel, Holger; Beck, Fabian and Dachsbacher, Carsten and Sadlo, Filip
    The parallel vectors operator is a prominent tool in visualization that has been used for line feature extraction in a variety of applications such as ridge and valley lines, separation and attachment lines, and vortex core lines. It yields all points in a 3D domain where two vector fields are parallel. We extend this concept to the space of tensor fields, by introducing the parallel eigenvectors (PEV) operator. It yields all points in 3D space where two tensor fields have real parallel eigenvectors. Similar to the parallel vectors operator, these points form structurally stable line structures. We present an algorithm for extracting these lines from piecewise linear tensor fields by finding and connecting all intersections with the cell faces of a data set. The core of the approach is a simultaneous recursive search both in space and on all possible eigenvector directions. We demonstrate the PEV operator on different analytic tensor fields and apply it to several data sets from structural mechanics simulations.