Nonphotorealistic Visualization of Multidimensional Datasets

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Title: Nonphotorealistic Visualization of Multidimensional Datasets
Author: Tateosian, Laura Gray
Advisors: Dr. Chris G Healey, Committee Chair
Dr. Edward W. Davis, Committee Member
Dr. Robert St Amant, Committee Member
Abstract: The huge quantities of data that are being recorded annually need to be organized and ana-lyzed. The datasets often consist of a large number of elements, each associated with multiple attributes. Our objective is to create effective, aesthetically appealing multidimensional visual-izations. By mapping element attributes to carefully chosen visual features, such visualizations support exploration, encourage prolonged inspection, and facilitate discovery of unexpected data characteristics and relationships. We present a new visualization technique that uses painted brushstrokes to represent data elements of large multidimensional datasets. Each element s attributes controls the visual features of one or more brushstrokes. To pursue aesthetic appeal, we draw inspiration from the Impressionist style of painting and apply rendering techniques from nonphotorealistic graphics. We construct our mappings to harness the strengths of the human visual system. The resulting displays are nonphotorealistic visualizations of the information in the datasets. Studies confirm that existing guidelines based on human visual perception apply to our painterly styles. Additional studies investigate the artistic appeal of our visualizations, along with the emotional and visual features that influence aesthetic judgments. The results of these studies are combined to build a tool which creates visualizations that are both effective and aesthetic. Finally, we apply our method to a real-world dataset.
Date: 2003-01-26
Degree: MS
Discipline: Computer Science

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