Texturing of Layered Surfaces for Optimal Viewing
IEEE Transactions on Visualization and Computer Graphics
Subjective Quantification of Perceptual Interactions among some 2D Scientific Visualization Methods
IEEE Transactions on Visualization and Computer Graphics
Evaluation of glyph-based multivariate scalar volume visualization techniques
Proceedings of the 6th Symposium on Applied Perception in Graphics and Visualization
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This dissertation addresses an important problem in the visualization of scientific data: Given a family of single-valued functions F k(x,y) in two dimensions, all sampled on a regular, finite, two-dimensional grid, i, j, devise visualizations that allow each function to be visually discriminated and studied separately and studied for comparisons and correlations with other functions in the family. I developed a technique called Data-Driven Spots ( DDS), where each function Fk( x,y) is sampled, by a random collection of circular Gaussians with a uniform standard deviation, rather than presented in full. Human visual perception estimates the function's values where it is not represented in the sample. These sampled functions, called layers, are presented overlaid. Each function is sampled and displayed in some regions where the others are not, thus each can be discriminated separately; since all are shown simultaneously, correlations can also be estimated. Layers are displayed with alpha-blending, such that each layer is distinguished by hue and/or the standard deviation of the Gaussians. Data values are multiplied by the Gaussian at each i,j grid point; the result determines the opacity of that layer at that point.Blended with a neutral gray background, each layer has color saturation at i,j proportional to the modulated data value. Since opacities are displayed, lower layers are mostly visible between the spots on upper layers. I built a DDS Toolkit that enables users to construct DDS visualizations of function families. Construction of effective DDS visualizations requires interactive exploration of visualization parameters, which the toolkit facilitates. I used the toolkit to prepare visualizations of many kinds of function families; a collection of images is presented. To evaluate the effectiveness of the DDS technique, I performed user studies. These studies showed that performance on a spatial correlation task was better for overlaid DDS images than side-by-side DDS images, that alpha-blended layers are visually discriminable in the presence of up to seven distractors, and that animation of layers with respect to each other dramatically increases their visual salience. Animation permits a Fk( x,y) to be seen at every i,j over time; human visual perception integrates non-simultaneously displayed values into a coherent understanding.* *This dissertation is compound (contains both a paper copy and CD a part of the dissertation).