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WYSIWYP: What You See Is What You Pick

Published: 01 December 2012 Publication History

Abstract

Scientists, engineers and physicians are used to analyze 3D data with slice-based visualizations. Radiologists for example are trained to read slices of medical imaging data. Despite the numerous examples of sophisticated 3D rendering techniques, domain experts, who still prefer slice-based visualization do not consider these to be very useful. Since 3D renderings have the advantage of providing an overview at a glance, while 2D depictions better serve detailed analyses, it is of general interest to better combine these methods. Recently there have been attempts to bridge this gap between 2D and 3D renderings. These attempts include specialized techniques for volume picking in medical imaging data that result in repositioning slices. In this paper, we present a new volume picking technique called WYSIWYP (“what you see is what you pick”) that, in contrast to previous work, does not require pre-segmented data or metadata and thus is more generally applicable. The positions picked by our method are solely based on the data itself, the transfer function, and the way the volumetric rendering is perceived by the user. To demonstrate the utility of the proposed method, we apply it to automated positioning of slices in volumetric scalar fields from various application areas. Finally, we present results of a user study in which 3D locations selected by users are compared to those resulting from WYSIWYP. The user study confirms our claim that the resulting positions correlate well with those perceived by the user.

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  • (2024)MeTACAST: Target- and Context-Aware Spatial Selection in VRIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332651730:1(480-494)Online publication date: 1-Jan-2024
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cover image IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics  Volume 18, Issue 12
December 2012
922 pages

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IEEE Educational Activities Department

United States

Publication History

Published: 01 December 2012

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Cited By

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  • (2024)SpatialTouch: Exploring Spatial Data Visualizations in Cross-RealityIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345636831:1(897-907)Online publication date: 10-Sep-2024
  • (2024)BEMTrace: Visualization-Driven Approach for Deriving Building Energy Models from BIMIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345631531:1(240-250)Online publication date: 23-Sep-2024
  • (2024)MeTACAST: Target- and Context-Aware Spatial Selection in VRIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332651730:1(480-494)Online publication date: 1-Jan-2024
  • (2024)Monocular Depth Decomposition of Semi-Transparent Volume RenderingsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.324530530:7(3981-3994)Online publication date: 1-Jul-2024
  • (2019)Histogram-Based Nonlinear Transfer Function Edit and FusionImage and Graphics10.1007/978-3-030-34110-7_26(300-315)Online publication date: 23-Aug-2019
  • (2018)Screen space particle selectionProceedings of the Conference on Computer Graphics & Visual Computing10.2312/cgvc.20181208(61-69)Online publication date: 13-Sep-2018
  • (2016)Spatial opacity maps for direct volume rendering of regions of interestProceedings of the XXVI Spanish Computer Graphics Conference10.5555/3056989.3056996(23-30)Online publication date: 13-Sep-2016
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  • (2015)Understanding the impact of visual representation restrictiveness on experience sharingJournal of Visual Languages and Computing10.1016/j.jvlc.2015.09.00331:PA(30-46)Online publication date: 1-Dec-2015
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