Name
Abstract | ||
|---|---|---|
Research in human visual cognition suggests that beautiful images can engage the visual system, encouraging it to linger in certain locations in an image and absorb subtle details. By developing aesthetically pleasing visualizations of data, we aim to engage viewers and promote prolonged inspection, which can lead to new discoveries within the data. We present three new visualization techniques that apply painterly rendering styles to vary interpretational complexity (IC), indication and detail (ID), and visual complexity (VC), image properties that are important to aesthetics. Knowledge of human visual perception and psychophysical models of aesthetics provide the theoretical basis for our designs. Computational geometry and nonphotorealistic algorithms are used to preprocess the data and render the visualizations. We demonstrate the techniques with visualizations of real weather and supernova data. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1274871.1274886 | NPAR |
Keywords | Field | DocType |
human visual cognition,image property,human visual perception,engaging viewer,new visualization technique,interpretational complexity,supernova data,nonphotorealistic visualization,visual system,new discovery,visual complexity,beautiful image,voronoi diagram,voronoi diagrams,aesthetics,visualization | Visual complexity,Visual cognition,Computer vision,Computer graphics (images),Human visual perception,Visualization,Computer science,Computational geometry,Image properties,Artificial intelligence,Painterly rendering,Creative visualization | Conference |
Citations | PageRank | References |
9 | 0.65 | 28 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laura G. Tateosian | 1 | 10 | 1.09 |
| Christopher G. Healey | 2 | 861 | 65.46 |
| James T. Enns | 3 | 414 | 29.02 |