The Line integral Convolution (LIC) method, which blurs white noise textures along a vector field, is an effective way to visualize overailfiow patterns in a 2D domain.2 The methodproduces a flow texture image based on the input velocityfield defined in the domain. Because ofthe nature of the algorithm, the texture image tends to be blurry. This sometimes makes it difficult to identify boundaries whereflow separation and reattachments occur. We present techniques to enhance LJC texture images and use colored texture images to highlightflow separation and reattachment boundaries. Our techniques have been applied to severalflowfields defined in 3D curvilinear multi-block grids and scientists havefound the results to be very useful.
Box plot is a compact representation that encodes the minimum, maximum, mean, median, and quartile information of a distribution. In practice, a single box plot is ttrawn for each variable of interest. With the advent of more accessible computing power, we are now facing the problem of visualizing data where there is a distribution at each 2D spatial location. Simply extending the box plot technique to distributions over 2D domain is not straightforward. One challenge is reducing the visual clutter if a box plot is drawn over each grid location in the 2D domain.This paper presents and discusses two general approaches, using parametric statistics and shape descriptors, to present 2D distribution data sets. Both approaches provide additional insights ,.:ompared to the traditional box plot technique.
We discuss a fusion-based visualization method to analyze a multivariate climate dataset and its metadata. The primary difference between a conventional visualization and a fusion-based visualization is that the former draws on a single image whereas the latter draws on multiple see-through layers, which are then overlaid on each other to form the final visualization. We propose optimized colormaps to highlight subtle features that would not be shown with conventional colormaps. We present fusion techniques that integrate multiple single-purpose visualization techniques into the same viewing space. Our highly flexible fusion approach allows scientists to explore multiple parameters concurrently by mixing and matching images without frequently reconstructing new visualizations from the data for every possible combination. Although our primary visualization application is climate modeling, we show with examples that our fundamental design -fusing layers of data images for multivariate visualization -can be generalized for other information visualization applications.
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