Novel Texture-based Visualization Methods for High-dimensional Multi- field Data Sets.

摘要

Technological advances over the past decade have enabled scientists to create ever larger and more complex scientific data sets. The main challenge in Scientific Visualization is mapping high-dimensional data onto a 2D display with only 3 perceptual dimensions (RGB colors). In this research we propose and evaluate methods for creating and combining textures for multi-field visualization. We have identified four classes of post-processing techniques for combining textural representations of field data: blending, overlay, bump mapping, and masking. We evaluated the effectiveness of these methods by using them for visualizing multiple vector fields using Line Integral Convolution (LIC) textures. Our user study suggests that blending is the most effective technique to combine multiple vector field visualization textures, while masking performs worst. There is some evidence that visualizations with smooth color changes are perceived as visually more attractive, and that aesthetics increases the perceived effectiveness of a visualization technique. Based on these results we have created a new generative formal grammar called texture grammar that combines implementations of existing visualization techniques and image compositing techniques to automatically produce visualizations, depending on the input fields.