Stevens dot patterns for 2D flow visualization

Authors:
Laura G. Tateosian;Brent M. Dennis;Christopher G. Healey
Affiliations:
North Carolina State University;North Carolina State University;North Carolina State University
Venue:
APGV '06 Proceedings of the 3rd symposium on Applied perception in graphics and visualization
Year:
2006

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Abstract

This paper describes a new technique to visualize 2D flow fields with a sparse collection of dots. A cognitive model proposed by Kent Stevens describes how spatially local configurations of dots are processed in parallel by the low-level visual system to perceive orientations throughout the image. We integrate this model into a visualization algorithm that converts a sparse grid of dots into patterns that capture flow orientations in an underlying flow field. We describe how our algorithm supports large flow fields that exceed the capabilities of a display device, and demonstrate how to include properties like direction and velocity in our visualizations. We conclude by applying our technique to 2D slices from a simulated supernova collapse.