Perceptually based brush strokes for nonphotorealistic visualization

  • Authors:

  • Christopher G. Healey;Laura Tateosian;James T. Enns;Mark Remple

  • Affiliations:

  • North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC;The University of British Columbia, Vancouver, B.C., Canada;The University of British Columbia, Vancouver, B.C., Canada

  • Venue:
  • ACM Transactions on Graphics (TOG)
  • Year:
  • 2004

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Abstract

An important problem in the area of computer graphics is the visualization of large, complex information spaces. Datasets of this type have grown rapidly in recent years, both in number and in size. Images of the data stored in these collections must support rapid and accurate exploration and analysis. This article presents a method for constructing visualizations that are both effective and aesthetic. Our approach uses techniques from master paintings and human perception to visualize a multidimensional dataset. Individual data elements are drawn with one or more brush strokes that vary their appearance to represent the element's attribute values. The result is a nonphotorealistic visualization of information stored in the dataset. Our research extends existing glyph-based and nonphotorealistic techniques by applying perceptual guidelines to build an effective representation of the underlying data. The nonphotorealistic properties the strokes employ are selected from studies of the history and theory of Impressionist art. We show that these properties are similar to visual features that are detected by the low-level human visual system. This correspondence allows us to manage the strokes to produce perceptually salient visualizations. Psychophysical experiments confirm a strong relationship between the expressive power of our nonphotorealistic properties and previous findings on the use of perceptual color and texture patterns for data display. Results from these studies are used to produce effective nonphotorealistic visualizations. We conclude by applying our techniques to a large, multidimensional weather dataset to demonstrate their viability in a practical, real-world setting.