Conference
C. G. Healey, J. T. Enns
Proceedings IEEE Visualization '98, 1998, pp. 111-118
Proceedings IEEE Visualization '98, 1998, pp. 111-118
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APA
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Healey, C. G., & Enns, J. T. (1998). Building perceptual textures to visualize multidimensional datasets. In Proceedings IEEE Visualization '98 (pp. 111–118).
Chicago/Turabian
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Healey, C. G., and J. T. Enns. “Building Perceptual Textures to Visualize Multidimensional Datasets.” In Proceedings IEEE Visualization '98, 111–118, 1998.
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Healey, C. G., and J. T. Enns. “Building Perceptual Textures to Visualize Multidimensional Datasets.” Proceedings IEEE Visualization '98, 1998, pp. 111–18.
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@conference{c1998a,
title = {Building perceptual textures to visualize multidimensional datasets},
year = {1998},
pages = {111-118},
author = {Healey, C. G. and Enns, J. T.},
booktitle = {Proceedings IEEE Visualization '98}
}
Abstract
This paper presents a new method for using texture to visualize multi-dimensional data elements arranged on an underlying 3D height field. We hope to use simple texture patterns in combination with other visual features like hue and intensity to increase the number of attribute values we can display simultaneously. Our technique builds perceptual texture elements (or pexels) to represent each data element. Attribute values encoded in the data element are used to vary the appearance of a corresponding pexel. Texture patterns that form when the pexels are displayed can be used to rapidly and accurately explore the dataset. Our pexels are built by controlling three separate texture dimensions: height, density and regularity. Results from computer graphics, computer vision and cognitive psychology have identified these dimensions as important for the formation of perceptual texture patterns. We conducted a set of controlled experiments to measure the effectiveness of these dimensions, and to identify any visual interference that may occur when all three are displayed simultaneously at the same spatial location. Results from our experiments show that these dimensions can be used in specific combinations to form perceptual textures for visualizing multidimensional datasets. We demonstrate the effectiveness of our technique by applying it to two real-world visualization environments: tracking typhoon activity in southeast Asia, and analyzing ocean conditions in the northern Pacific.
