Victoria Interrante.
Harnessing Natural Textures for Multivariate Visualization.
In IEEE Computer Graphics and Applications, vol. 20, no. 6, pp. 6--11, 2000.

Links:

Abstract:

In our ongoing quest to convey more information more clearly in a single image, harnessing the full potential of texture for data representation remains an elusive goal. Others have begun excellent work in this area,1-3 and my efforts are inspired by their example. The grail that I seek is a partially ordered multidimensional palette of richly detailed and varying texture patterns that can be used in conjunction with lightness and hue to represent multivariate information. The goal is to facilitate the flexible visual appreciation of the correlations of various quantities across the different dimensions. The approach that I outline here departs a bit from the norm, but is motivated by a desire to proceed more directly from my vision of what I want to achieve, unrestrained by the limitations of the tools I have on hand. In the following discussion, I motivate the adoption of rich, natural textures resembling those from photographic images4 as elemental primitives and sketch some of the approaches that we can take to enhance our understanding of how to effectively harness their properties. My intent here is not to present results, but to expound on the issues and conclude with the questions to which we re still seeking answers.

Bibtex:

@Article{ 	interrante:2000:HNTV,
  author = 	{Victoria Interrante},
  title = 	{Harnessing Natural Textures for Multivariate
                  Visualization},
  journal = 	{{IEEE} Computer Graphics and Applications},
  volume = 	{20},
  number = 	{6},
  pages = 	{6--11},
  month = 	{November/December},
  year = 	{2000},
}

Images:

References:

C.G. Healey and J.T. Enns, "Large Datasets at a Glance: Combining Textures and Colors in Scientific Visualization," IEEE Trans. Visualization and Computer Graphics, Vol. 5, No. 2, 1999, pp. 145-167.
D.H. Laidlaw et al., "Visualizing Diffusion Tensor Images of the Mouse Spinal Cord," Proc. IEEE Visualization 98, ACM Press, New York, 1998, pp. 127-134.
C. Ware and W. Knight, "Using Visual Texture for Infor- mation Display," ACM Trans. Graphics, Vol. 14, No. 1, 1995, pp. 3-20.
P. Brodatz, Textures: A Photographic Album for Artists and Designers, Dover Publications, New York, 1966.
E.R. Tufte, The Visual Display of Quantitative Information, Graphics Press, Cheshire, Conn., 1983, p. 111.
A.R. Rao and G.L. Lohse, "Identifying High Level Features of Texture Perception," CVGIP: Graphical Models and Image Processing, Vol. 55, No. 3, 1993, pp. 218-233.
J.S. Payne, L. Hepplewhite, and T.J. Stonham, Texture Similarity: Using Human Studies to Interpret Retrieval Results, Tech. Report TR-685, Dept. Electronic and Computer Engineering, Brunel University, UK, 1997.
Y. Rubner and C. Tomasi, "Texture Metrics," Proc. IEEE Int'l Conf. Sys- tems, Man, and Cybernetics, IEEE Press, Piscataway, N.J., 1998, pp. 4601-4607.
H-C. Nothdurft "Salience from Feature Contrast: Additivity Across Dimensions," Vision Research, Vol. 40, 2000, pp. 1183-1201.
C.G. Healey, "Choosing Effective Colors for Data Visualization," Proc. IEEE Visualization 96, ACM Press, New York, 1996, pp. 263-270.
J. Portilla and E.P. Simoncelli, "A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients," Int'l J. of Computer Vision, 2000, to appear; currently available on the Web at http://www .cns.nyu.edu/ftp/eero/portilla99.pdf.
S.-C. Zhu et al., "Exploring Texture Ensembles by Efficient Markov Chain Monte Carlo-Toward a 'Trichromacy' Theory of Texture," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 22, No. 6, 2000, pp. 554-569.
A.A. Efros and T.K. Leung, "Texture Synthesis by Nonparametric Sam- pling," IEEE Int'l Conf. Computer Vision, Volume II, IEEE Press, Piscat- away, N.J., 1999, pp. 1033-1038.
1992 Agricultural Atlas of the United States, U.S. Census Bureau Geogra- phy Div. Map Gallery, http://www.census.gov/geo/www/map-Gallery.