Browsing by Author "Hornbeck, Haysn"

Now showing 1 - 4 of 4
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    Smashing Rays: Combining Realistic and Illustrative Visualization
    (2018-10) Hornbeck, Haysn; Alim, Usman
    Traditional visualization techniques for volumetric datasets are ex- cellent at revealing subtle details, but have difficulty conveying the physicality of a dataset. By using modern rendering techniques and physical models as a guideline, the authors attempt a visualization of an asteroid impact dataset which incorporates realism alongside illustrative techniques.
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    Spatial Partitioning for Distributed Path-Tracing Workloads
    (2018-09-21) Hornbeck, Haysn; Alim, Usman Raza; Gavrilova, Marina L.; Chan, Sonny
    The literature on path tracing has rarely explored distributing workload using distinct spatial partitions. This thesis corrects that by describing seven algorithms which use Voronoi cells to partition scene data. They were tested by simulating their performance with real-world data, and fitting the results to a model of how such partitions should behave. Analysis shows that image-centric partitioning outperforms other algorithms, with a few exceptions, and restricting Voronoi centroid movement leads to more efficient algorithms. The restricted algorithms also demonstrate excellent scaling properties. Potential refinements are discussed, such as voxelization and locality, but the tested algorithms are worth further exploration. The details of an implementation are outlined, as well.
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    UofC-Bayes: A Bayesian Approach to Visualizing Uncertainty in Likert Scales
    (2019-10) Hornbeck, Haysn; Alim, Usman
    Disasters demand a quick response based on incomplete information. For the Saint Himark dataset, part of the 2019 VAST Challenge, we focused on delivering a visualization which accurately conveyed that uncertainty. While our analysis was done offline, we chose techniques and algorithms which could easily be applied to realtime usage. Our visualization for the first mini-challenge was a one-screen dashboard that summarized citizen feedback.
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    UofC-Bayes: A Bayesian Approach to Visualizing Uncertainty in Radiation Data
    (2019-10) Hornbeck, Haysn; Alim, Usman
    Disasters demand a quick response based on incomplete information. For the Saint Himark dataset, part of the 2019 VAST Challenge, we focused on delivering a visualization which accurately conveyed that uncertainty. While our analysis was done offline, we chose techniques and algorithms which could easily be applied to real-time usage. Our visualization for the second mini-challenge was two separate screens for two separate tasks: a broad overview of radiation levels, and a detailed look at specific sensors.