EuroVis13: Eurographics Conference on Visualizationhttps://diglib.eg.org:443/handle/10.2312/138882024-03-28T09:20:36Z2024-03-28T09:20:36ZInteractive Ray Casting of Geodesic GridsXie, JinrongYu, HongfengMa, Kwan-Liuhttps://diglib.eg.org:443/handle/10.1111/v32i3pp481-4902022-03-28T08:28:02Z2013-01-01T00:00:00ZInteractive Ray Casting of Geodesic Grids
Xie, Jinrong; Yu, Hongfeng; Ma, Kwan-Liu
B. Preim, P. Rheingans, and H. Theisel
Geodesic grids are commonly used to model the surface of a sphere and are widely applied in numerical simulations of geoscience applications. These applications range from biodiversity, to climate change and to ocean circulation. Direct volume rendering of scalar fields defined on a geodesic grid facilitates scientists in visually understanding their large scale data. Previous solutions requiring to first transform the geodesic grid into another grid structure (e.g., hexahedral or tetrahedral grid) for using graphics hardware are not acceptable for large data, because such approaches incur significant computing and storage overhead. In this paper, we present a new method for efficient ray casting of geodesic girds by leveraging the power of Graphics Processing Units (GPUs). A geodesic grid can be directly fetched from storage or streamed from simulations to the rendering stage without the need of any intermediate grid transformation. We have designed and implemented a new analytic scheme to efficiently perform value interpolation for ray integration and gradient calculations for lighting. This scheme offers a more cost-effective rendering solution over the existing direct rendering approach. We demonstrate the effectiveness of our rendering solution using real-world geoscience data.
2013-01-01T00:00:00ZSynthetic BrainbowsWan, YongOtsuna, HideoHansen, Charleshttps://diglib.eg.org:443/handle/10.1111/v32i3pp471-4802022-03-28T08:27:56Z2013-01-01T00:00:00ZSynthetic Brainbows
Wan, Yong; Otsuna, Hideo; Hansen, Charles
B. Preim, P. Rheingans, and H. Theisel
Brainbow is a genetic engineering technique that randomly colorizes cells. Biological samples processed with this technique and imaged with confocal microscopy have distinctive colors for individual cells. Complex cellular structures can then be easily visualized. However, the complexity of the Brainbow technique limits its applications. In practice, most confocal microscopy scans use different florescence staining with typically at most three distinct cellular structures. These structures are often packed and obscure each other in rendered images making analysis difficult. In this paper, we leverage a process known as GPU framebuffer feedback loops to synthesize Brainbow-like images. In addition, we incorporate ID shuffling and Monte-Carlo sampling into our technique, so that it can be applied to single-channel confocal microscopy data. The synthesized Brainbow images are presented to domain experts with positive feedback. A user survey demonstrates that our synthetic Brainbow technique improves visualizations of volume data with complex structures for biologists.
2013-01-01T00:00:00ZTrajectoryLenses - A Set-based Filtering and Exploration Technique for Long-term Trajectory DataKrüger, RobertThom, DennisWörner, MichaelBosch, HaraldErtl, Thomashttps://diglib.eg.org:443/handle/10.1111/v32i3pp451-4602022-03-28T08:28:03Z2013-01-01T00:00:00ZTrajectoryLenses - A Set-based Filtering and Exploration Technique for Long-term Trajectory Data
Krüger, Robert; Thom, Dennis; Wörner, Michael; Bosch, Harald; Ertl, Thomas
B. Preim, P. Rheingans, and H. Theisel
The visual analysis of spatiotemporal movement is a challenging task. There may be millions of routes of different length and shape with different origin and destination, extending over a long time span. Furthermore there can be various correlated attributes depending on the data domain, e.g. engine measurements for mobility data or sensor data for animal tracking. Visualizing such data tends to produce cluttered and incomprehensible images that need to be accompanied by sophisticated filtering methods. We present TrajectoryLenses, an interaction technique that extends the exploration lens metaphor to support complex filter expressions and the analysis of long time periods. Analysts might be interested only in movements that occur in a given time range, traverse a certain region, or end at a given area of interest (AOI). Our lenses can be placed on an interactive map to identify such geospatial AOIs. They can be grouped with set operations to create powerful geospatial queries. For each group of lenses, users can access aggregated data for different attributes like the number of matching movements, covered time, or vehicle performance. We demonstrate the applicability of our technique on a large, real-world dataset of electric scooter tracks spanning a 2-year period.
2013-01-01T00:00:00ZViviSection: Skeleton-based Volume EditingKarimov, AlexeyMistelbauer, GabrielSchmidt, JohannaMindek, PeterSchmidt, ElisabethSharipov, TimurBruckner, StefanGröller, Eduardhttps://diglib.eg.org:443/handle/10.1111/v32i3pp461-4702022-03-28T08:28:11Z2013-01-01T00:00:00ZViviSection: Skeleton-based Volume Editing
Karimov, Alexey; Mistelbauer, Gabriel; Schmidt, Johanna; Mindek, Peter; Schmidt, Elisabeth; Sharipov, Timur; Bruckner, Stefan; Gröller, Eduard
B. Preim, P. Rheingans, and H. Theisel
Volume segmentation is important in many applications, particularly in the medical domain. Most segmentation techniques, however, work fully automatically only in very restricted scenarios and cumbersome manual editing of the results is a common task. In this paper, we introduce a novel approach for the editing of segmentation results. Our method exploits structural features of the segmented object to enable intuitive and robust correction and verification. We demonstrate that our new approach can significantly increase the segmentation quality even in difficult cases such as in the presence of severe pathologies.
2013-01-01T00:00:00Z