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        "title": "How to represent landmark trees in digital 3D maps? An automated workflow and user study",
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        "title": "Echtzeitvisualisierung von Lawinenrisiko basierend auf hochauflösenden Geodaten",
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        "abstract": "Um das Lawinenrisiko auf Touren abzuschätzen, konsultieren Tourengeher·innen typischerweise vorab den aktuellen Lawinenlagebericht (LLB) sowie die Geländeeigenschaften, wie Hangneigung, Höhe und Exposition der geplanten Tour auf einer Karte. Reduktionsmethoden wie Stop-or-Go oder die SnowCard können sowohl bei der Planung als auch vor Ort angewandt werden, um das Risiko abzuschätzen. Bei korrekter Anwendung dieser Methoden könnte ein Großteil der Todesfälle vermieden werden. Die Anwendung umfasst jedoch mehrere kognitiv aufwändige Schritte: Im ersten Schritt müssen Tourengeher·innen die Informationen aus LLB und Karte korrekt verknüpfen und anhand der gewählten Methode interpretieren, um potenziell kritische Regionen entlang der Route vorab identifizieren zu können. Im zweiten Schritt müssen potenziell kritische Regionen auch während der Tour als solche wiedererkannt und vor Ort beurteilt werden. \nUm die Anwendung von Reduktionsmethoden für Wintersportler·innen zu vereinfachen, können die Informationen aus LLB computergestützt mit den Geländeeigenschaften ausgewertet und direkt in einer Karte dargestellt werden. Skitourenguru, beispielsweise, berechnet das Lawinenrisiko entlang vorgegebener Routen und stellt diese in einer 2D Karte dar. Im Vergleich zu 2D Karten erleichtert eine dreidimensionale Darstellung jedoch die Interpretation des Geländes und das Finden von Routen. Unsere Hypothese ist daher, dass eine direkte Visualisierung des Lawinenrisikos auf einer detaillierten 3D Karte die Identifikation von potenziell kritischen Regionen einer Route in der Planungsphase, sowie deren Wiedererkennung während der Tour, erleichtert.\nWir stellen eine integrierte 3D Risikovisualisierung vor, welche Daten aus dem aktuellen LLB mit einem hochauflösenden Geländemodell kombiniert und existierende Reduktionsmethoden in Echtzeit auswertet, um das Ergebnis auf einer interaktiven Webseite zu visualisieren.\n",
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        "title": "Real-Time Avalanche Risk Visualization on a Large-Scale Geospatial Dataset",
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        "abstract": "Every winter season reports of fatal avalanche accidents in the Alps are part of the news cycle. Data for tour planning with avalanche risk evaluation is available to recreationists in the form of daily avalanche reports and outdoor maps. These data are, however, distributed across different sources and have to be manually integrated by the end user to arrive at a risk value for a given tour. Risk reduction methods provide a framework for this integration process and thereby allow mountaineers to judge the overall risk and determine potential high-risk areas beforehand. We present an integrated risk visualization tool to support risk-averse tour planning for backcountry skiing. Based on a high-resolution Digital Elevation Model (DEM), our visualization displays avalanche risk levels in real-time as a web-based 2.5D map application. Different static and dynamic avalanche risk layers are rendered on the Graphics Processing Unit (GPU) covering the alpine regions of Austria. By implementing a prototype application, we show that reduction methods can be evaluated in real-time based on existing data sources consisting of a Digital Elevation Model (DEM) and the per-region avalanche report for Austria. This evaluation allows us to visualize localized avalanche risk for a large area. To evaluate our prototype visualization, we conducted a pilot user study. The results of the study show that users have low trust in an integrated risk visualization when they are not familiar with the underlying risk reduction method. However, results also indicate that the combination of a 2.5D map with our integrated risk layer facilitates the identification of potential high-risk areas. We conclude that our work provides a foundation for an integrated risk avalanche risk visualization, however, further validation steps are still necessary.",
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        "title": "Illustrative Motion Smoothing for Attention Guidance in Dynamic Visualizations",
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        "abstract": "3D animations are an effective method to learn about complex dynamic phenomena, such as mesoscale biological processes. The animators’ goals are to convey a sense of the scene’s overall complexity while, at the same time, visually guiding the user through a story of subsequent events embedded in the chaotic environment. Animators use a variety of visual emphasis techniques to guide the observers’ attention through the story, such as highlighting, halos – or by manipulating motion parameters of the scene. In this paper, we investigate the effect of smoothing the motion of contextual scene elements to attract attention to focus elements of the story exhibiting high-frequency motion. We conducted a crowdsourced study with 108 participants observing short animations with two illustrative motion smoothing strategies: geometric smoothing through noise reduction of contextual motion trajectories and visual smoothing through motion blur of context items. We investigated the observers’ ability to follow the story as well as the effect of the techniques on speed perception in a molecular scene. Our results show that moderate motion blur significantly improves users’ ability to follow the story. Geometric motion smoothing is less effective but increases the visual appeal of the animation. However, both techniques also slow down the perceived speed of the animation. We discuss the implications of these results and derive design guidelines for animators of complex dynamic visualizations.",
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        "title": "Generating Molecular Motion Blur Videos for a User Study",
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    {
        "id": "ESCHNER-2019-GDT",
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        "repositum_id": null,
        "title": "Generating Synthetic Training Data for Video Surveillance Applications",
        "date": "2019-10",
        "abstract": "As the demand for ever-more capable computer vision systems has been increasing in\nrecent years, there is a growing need for labeled ground-truth data for such systems.\nThese ground-truth datasets are used for the training and evaluation of computer vision\nalgorithms and are usually created by manually annotating images or image sequences\nwith semantic labels. Synthetic video generation provides an alternative approach to\nthe problem of generating labels. Here, the label data and the image sequences can be\ncreated simultaneously by utilizing a 3D render engine. Many of the existing frameworks\nfor generating such synthetic datasets focus the context of autonomous driving, where\nvast amounts of labeled input data are needed.\nIn this thesis an implementation of a synthetic data generation framework for evaluating\ntracking algorithms in the context of video surveillance is presented. This framework uses\na commercially available game engine as a renderer to generate synthetic video clips that\ndepict different scenarios that can occur in a video surveillance setting. These scenarios\ninclude a multitude of interactions of different characters in a reconstructed environment.\nA collection of such synthetic clips is then compared to real videos by using it as an input\nfor two different tracking algorithms. While producing synthetic ground-truth data in\nreal time using a game engine is less work intensive than manual annotation, the results\nof the evaluation show that both tracking algorithms perform better on real data. This\nsuggests that the synthetic data coming from the framework is limited in its suitability\nfor evaluating tracking algorithms.",
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