PG2018 Short Papers and Posters

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https://diglib.eg.org/handle/10.2312/2632665

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Browsing PG2018 Short Papers and Posters by Subject "Human"

Now showing 1 - 2 of 2
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    Tabby: Explorable Design for 3D Printing Textures
    (The Eurographics Association, 2018) Suzuki, Ryo; Yatani, Koji; Gross, Mark D.; Yeh, Tom; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    This paper presents Tabby, an interactive and explorable design tool for 3D printing textures. Tabby allows texture design with direct manipulation in the following workflow: 1) select a target surface, 2) sketch and manipulate a texture with 2D drawings, and then 3) generate 3D printing textures onto an arbitrary curved surface. To enable efficient texture creation, Tabby leverages an auto-completion approach which automates the tedious, repetitive process of applying texture, while allowing flexible customization. Our user evaluation study with seven participants confirms that Tabby can effectively support the design exploration of different patterns for both novice and experienced users.
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    A Visual Analytics Approach for Traffic Flow Prediction Ensembles
    (The Eurographics Association, 2018) Kong, Kezhi; Ma, Yuxin; Ye, Chentao; Lu, Junhua; Chen, Xiqun; Zhang, Wei; Chen, Wei; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    Traffic flow prediction plays a significant role in Intelligent Transportation Systems (ITS). Due to the variety of prediction models, the prediction results form an intricate structure of ensembles and hence leave a challenge of understanding and evaluating the ensembles from different perspectives. In this paper, we propose a novel visual analytics approach for analyzing the predicted ensembles. Our approach models the uncertainty of different traffic flow prediction results. The variations of space, time, and network structures of those results are presented with the visualization designs. The visual interface provides a suite of interactions to enhance exploration of the ensembles. With the system, analysts can discover some intrinsic patterns in the ensemble. We use real-world urban traffic data to demonstrate the effectiveness of our system.