Christopher G. Healey - Stevens dot patterns for 2D flow visualization

Conference

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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APA Click to copy
Tateosian, L., Dennis, B., & Healey, C. G. (2006). Stevens dot patterns for 2D flow visualization. In 3rd International Symposium on Applied Perception in Graphics and Visualization (APGV '06) (pp. 15–58).

Chicago/Turabian Click to copy
Tateosian, L., B. Dennis, and C. G. Healey. “Stevens Dot Patterns for 2D Flow Visualization.” In 3rd International Symposium on Applied Perception in Graphics and Visualization (APGV '06), 15–58, 2006.

MLA Click to copy
Tateosian, L., et al. “Stevens Dot Patterns for 2D Flow Visualization.” 3rd International Symposium on Applied Perception in Graphics and Visualization (APGV '06), 2006, pp. 15–58.

BibTeX Click to copy

@conference{l2006a,
  title = {Stevens dot patterns for 2D flow visualization},
  year = {2006},
  pages = {15-58},
  author = {Tateosian, L. and Dennis, B. and Healey, C. G.},
  booktitle = {3rd International Symposium on Applied Perception in Graphics and Visualization (APGV '06)}
}

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.