Browsing by Author "Ammi, Mehdi"

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    Augmenting Tactile 3D Data Navigation With Pressure Sensing
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Wang, Xiyao; Besançon, Lonni; Ammi, Mehdi; Isenberg, Tobias; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    We present a pressure-augmented tactile 3D data navigation technique, specifically designed for small devices, motivated by the need to support the interactive visualization beyond traditional workstations. While touch input has been studied extensively on large screens, current techniques do not scale to small and portable devices. We use phone-based pressure sensing with a binary mapping to separate interaction degrees of freedom (DOF) and thus allow users to easily select different manipulation schemes (e. g., users first perform only rotation and then with a simple pressure input to switch to translation). We compare our technique to traditional 3D-RST (rotation, scaling, translation) using a docking task in a controlled experiment. The results show that our technique increases the accuracy of interaction, with limited impact on speed. We discuss the implications for 3D interaction design and verify that our results extend to older devices with pseudo pressure and are valid in realistic phone usage scenarios.
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    Hybrid Touch/Tangible Spatial 3D Data Selection
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Besançon, Lonni; Sereno, Mickael; Yu, Lingyun; Ammi, Mehdi; Isenberg, Tobias; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    We discuss spatial selection techniques for three-dimensional datasets. Such 3D spatial selection is fundamental to exploratory data analysis. While 2D selection is efficient for datasets with explicit shapes and structures, it is less efficient for data without such properties. We first propose a new taxonomy of 3D selection techniques, focusing on the amount of control the user has to define the selection volume. We then describe the 3D spatial selection technique Tangible Brush, which gives manual control over the final selection volume. It combines 2D touch with 6-DOF 3D tangible input to allow users to perform 3D selections in volumetric data. We use touch input to draw a 2D lasso, extruding it to a 3D selection volume based on the motion of a tangible, spatially-aware tablet. We describe our approach and present its quantitative and qualitative comparison to state-of-the-art structure-dependent selection. Our results show that, in addition to being dataset-independent, Tangible Brush is more accurate than existing dataset-dependent techniques, thus providing a trade-off between precision and effort.