Texture-based visualization of uncertainty in flow fields

In this paper, we present two novel texture-based techniques to visualize uncertainty in time-dependent 2D flow fields. Both methods use semi-Lagrangian texture advection to show flow direction by streaklines and convey uncertainty by blurring these streaklines. The first approach applies a cross advection perpendicular to the flow direction. The second method employs isotropic diffusion that can be implemented by Gaussian filtering. Both methods are derived from a generic filtering process that is incorporated into the traditional texture advection pipeline. Our visualization methods allow for a continuous change of the density of flow representation by adapting the density of particle injection. All methods can be mapped to efficient GPU implementations. Therefore, the user can interactively control all important characteristics of the system like particle density, error influence, or dye injection to create meaningful illustrations of the underlying uncertainty. Even though there are many sources of uncertainties, we focus on uncertainty that occurs during data acquisition. We demonstrate the usefulness of our methods for the example of real-world fluid flow data measured with the particle image velocimetry (PIV) technique. Furthermore, we compare these techniques with an adapted multi-frequency noise approach.