Stevens dot patterns for 2D flow visualization

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.

• Type: Conference
• Year: 2006
• Where: APGV
• Authors: Laura Tateosian, Brent M. Dennis, Christopher G. Healey
• Keywords: APGV 2006, Dots, Sparse Collection, flow fields