Stevens dot patterns for 2D flow visualization


L. Tateosian, B. Dennis, C. G. Healey
3rd International Symposium on Applied Perception in Graphics and Visualization (APGV '06), 2006, pp. 15-58

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Tateosian, L., Dennis, B., & Healey, C. G. (2006). Stevens dot patterns for 2D flow visualization (pp. 15–58).

Tateosian, L., B. Dennis, and C. G. Healey. “Stevens Dot Patterns for 2D Flow Visualization.” In , 15–58, 2006.

Tateosian, L., et al. Stevens Dot Patterns for 2D Flow Visualization. 2006, pp. 15–58.


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.