Year: 2016

Using Data Indexing For Remote Visualization Of Point Cloud Data

Point cloud-based datasets have been critical to a number of research communities, including graphics, robotics, and CAD because in many ways, points better represent many data sources than triangulations. Visualizing points on remote devices remains somewhat problematic to this day. Very large datasets have the problem of needing to be transmitted, have any data structures built locally, and rendered on the local machine. This paper addresses the question of what's the point in rendering billions of points, when the display only has 2 million pixels? Continue reading

Muview: A Visual Analysis System For Exploring Uncertainty In Myocardial Ischemia Simulations

In this paper we describe the Myocardial Uncertainty Viewer (muView) system for exploring data stemming from the simulation of cardiac ischemia. The simulation uses a collection of conductivity values to understand how ischemic regions effect the undamaged anisotropic heart tissue. The data resulting from the simulation is multi-valued and volumetric, and thus, for every data point, we have a collection of samples describing cardiac electrical properties. View combines a suite of visual analysis methods to explore the area surrounding the ischemic zone and identify how perturbations of variables change the propagation of their effects. Continue reading

Rethinking Sensitivity Analysis Of Nuclear Simulations With Topology

In this paper, we design a framework for sensitivity analysis and visualization of multidimensional nuclear simulation data using partition-based, topology-inspired regression models and report on its efficacy. We rely on the established Morse-Smale regression technique, which allows us to partition the domain into monotonic regions where easily interpretable linear models can be used to assess the influence of inputs on the output variability. Continue reading

Critical Point Cancellation In 3D Vector Fields: Robustness And Discussion

This work introduces the first framework to directly cancel pairs or groups of 3D critical points in a hierarchical manner with guaranteed minimum amount of perturbation based on their robustness, a quantitative measure of their stability. In addition, our framework does not require the extraction of the entire 3D topology, which contains nontrivial separation structure, and thus is computationally effective. Furthermore, our algorithm can remove critical points in any subregions of the domain whose degree is zero and handle complex boundary configurations, making it capable of addressing challenging scenarios that may not be resolved otherwise. Continue reading

Correlation Coordinate Plots: Efficient Layouts For Correlation Tasks

We propose a new correlation task-specific visual design called Correlation Coordinate Plots (CCPs). CCPs transform data into a powerful coordinate system for estimating the direction and strength of correlation. To extend the functionality of this approach to multiple attribute datasets, we propose the Snowflake Visualization, a focus+context layout for exploring all pairwise correlations. Continue reading

Improved Identification Of Data Correlations Through Correlation Coordinate Plots

We propose a new correlation task-specific visualization method called Correlation Coordinate Plots (CCPs). CCPs transform data into a powerful coordinate system for estimating the direction and strength of correlation. To support multiple attributes, we propose the Snowflake Visualization, a focus+context layout for exploring all pairwise correlations. Continue reading