Oriented texture slivers: A technique for local value estimation of multiple scalar fields

Conference

C. Weigle, W. Emigh, G. Liu, R. M. Taylor, J. T. Enns, C. G. Healey
Proceedings Graphics Interface 2000 (GI 2000), 2000, pp. 163-170

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APA Click to copy
Weigle, C., Emigh, W., Liu, G., Taylor, R. M., Enns, J. T., & Healey, C. G. (2000). Oriented texture slivers: A technique for local value estimation of multiple scalar fields (pp. 163–170).

Chicago/Turabian Click to copy
Weigle, C., W. Emigh, G. Liu, R. M. Taylor, J. T. Enns, and C. G. Healey. “Oriented Texture Slivers: A Technique for Local Value Estimation of Multiple Scalar Fields.” In , 163–170. Proceedings Graphics Interface 2000 (GI 2000), 2000.

MLA Click to copy
Weigle, C., et al. Oriented Texture Slivers: A Technique for Local Value Estimation of Multiple Scalar Fields. 2000, pp. 163–70.

BibTeX Click to copy

@conference{c2000a,
  title = {Oriented texture slivers: A technique for local value estimation of multiple scalar fields},
  year = {2000},
  pages = {163-170},
  series = {Proceedings Graphics Interface 2000 (GI 2000)},
  author = {Weigle, C. and Emigh, W. and Liu, G. and Taylor, R. M. and Enns, J. T. and Healey, C. G.}
}

Abstract

Scanning electron microscope results with texture slivers whose orientations represent material type: Ca (15°), Cu (30°), Fe (60°), Mg (75°), O (90°), Mn (105°), S (150°), and Si (180°); and luminances represent the material's concentration

This paper describes a texture generation technique that combines orientation and luminance to support the simultaneous display of multiple overlapping scalar fields. Our orientations and luminances are selected based on psychophysical experiments that studied how the low-level human visual system perceives these visual features. The result is an image that allows viewers to identify data values in an individual field, while at the same time highlighting interactions between different fields. Our technique supports datasets with both smooth and sharp boundaries. It is stable in the presence of noise and missing values. Images are generated in real-time, allowing interactive exploration of the underlying data. Our technique can be combined with existing methods that use perceptual colours or perceptual texture dimensions, and can therefore be seen as an extension of these methods to further assist in the exploration and analysis of large, complex, multidimensional datasets.