Haidong Chen

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This paper presents an efficient visualization and exploration approach for modeling and characterizing the relationships and uncertainties in the context of a multidimensional ensemble dataset. Its core is a novel dissimilarity-preserving projection technique that characterizes not only the relationships among the mean values of the ensemble data objects(More)
Current energy-saving color design approaches can be classified into two categories, namely, context-aware dimming and color remapping. The former darkens individual regions with respect to the user interactions, and the latter replaces the color set with a new color set that yields lower energy consumption. Both schemes have drawbacks: color dimming tends(More)
Visualizing multivariate volume data is useful when the user wants to inspect the correlational distributions of multiple variables in a spatial field. Existing solutions commonly rely on color blending or weaving techniques to show multiple variables on a sampling point, probably causing heavy visual confusion. This paper presents an alternative solution(More)
Scatterplots are widely used to visualize scatter dataset for exploring outliers, clusters, local trends, and correlations. Depicting multi-class scattered points within a single scatterplot view, however, may suffer from heavy overdraw, making it inefficient for data analysis. This paper presents a new visual abstraction scheme that employs a hierarchical(More)
(a) (b) (c) (d) (e) Figure 1: Pairwise comparison for two brain datasets (top: 1248 fibers, bottom: 1622 fibers). From left to right: (a) the 3D fiber models; (b) 2D embedded points from multi-dimensional scaling projection of the fibers; (c) 2D embedded points from our approach; (d) the kernel density estimation maps based on (c) with Rainbow schemes; (e)(More)
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