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ACM Transactions on Graphics (TOG)
Conveying the 3D Shape of Smoothly Curving Transparent Surfaces via Texture
IEEE Transactions on Visualization and Computer Graphics
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Multivariate data visualization with data-driven spots
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APGV '05 Proceedings of the 2nd symposium on Applied perception in graphics and visualization
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APGV '05 Proceedings of the 2nd symposium on Applied perception in graphics and visualization
Perceptually optimizing textures for layered surfaces
APGV '05 Proceedings of the 2nd symposium on Applied perception in graphics and visualization
Strategies for the Visualization of Multiple 2D Vector Fields
IEEE Computer Graphics and Applications
A method for the perceptual optimization of complex visualizations
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GI '07 Proceedings of Graphics Interface 2007
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IEEE Transactions on Visualization and Computer Graphics
Grid With a View: Optimal Texturing for Perception of Layered Surface Shape
IEEE Transactions on Visualization and Computer Graphics
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Proceedings of the 6th Symposium on Applied Perception in Graphics and Visualization
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Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Non-Photorealistic Animation and Rendering
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Proceedings of the ACM SIGGRAPH Symposium on Applied Perception in Graphics and Visualization
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EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
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EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
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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.