Stevens dot patterns for 2D flow visualization

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.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.2 User/Machine Systems

Additional Classification:
  I. Computing Methodologies
  I.3 COMPUTER GRAPHICS
      I.3.3 Picture/Image Generation
          Subjects: Display algorithms

Keywords:
color, flow, motion, multidimensional, orientation, perception, vision, visualization