Texturing of Layered Surfaces for Optimal Viewing
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This paper is a contribution to the literature on perceptually optimal visualizations of layered three-dimensional surfaces. Specifically, we develop guidelines for generating texture patterns, which, when tiled on two overlapped surfaces, minimize confusion in depth-discrimination and maximize the ability to localize distinct features. We design a parameterized texture space and explore this texture space using a "human in the loop" experimental approach. Subjects are asked to rate their ability to identify Gaussian bumps on both upper and lower surfaces of noisy terrain fields. Their ratings direct a genetic algorithm, which selectively searches the texture parameter space to find fruitful areas. Data collected from these experiments are analyzed to determine what combinations of parameters work well and to develop texture generation guidelines. Data analysis methods include ANOVA, linear discriminant analysis, decision trees, and parallel coordinates. To confirm the guidelines, we conduct a post-analysis experiment, where subjects rate textures following our guidelines against textures violating the guidelines. Across all subjects, textures following the guidelines consistently produce high rated textures on an absolute scale, and are rated higher than those that did not follow the guidelines.
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Index Terms:
perception, optimal visualization, layered surfaces, human-in-the-loop, genetic algorithm, data mining, linear discriminant analysis, parallel coordinates, decision trees
Citation:
Alethea Bair, Donald H. House, Colin Ware, "Texturing of Layered Surfaces for Optimal Viewing," IEEE Transactions on Visualization and Computer Graphics, vol. 12, no. 5, pp. 1125-1132, Sept. 2006, doi:10.1109/TVCG.2006.183
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