Title
Visualization of uncertain scalar data fields using color scales and perceptually adapted noise
Abstract
We present a new method to visualize uncertain scalar data fields by combining color scale visualization techniques with animated, perceptually adapted Perlin noise. The parameters of the Perlin noise are controlled by the uncertainty information to produce animated patterns showing local data value and quality. In order to precisely control the perception of the noise patterns, we perform a psychophysical evaluation of contrast sensitivity thresholds for a set of Perlin noise stimuli. We validate and extend this evaluation using an existing computational model. This allows us to predict the perception of the uncertainty noise patterns for arbitrary choices of parameters. We demonstrate and discuss the efficiency and the benefits of our method with various settings, color maps and data sets.
Year
DOI
Venue
2011
10.1145/2077451.2077462
APGV
Keywords
Field
DocType
color scale visualization technique,animated pattern,local data value,perlin noise,uncertainty noise pattern,perlin noise stimulus,noise pattern,uncertain scalar data field,color map,scientific visualization,computer graphic,psychophysics,computer model,computer graphics
Data field,Computer vision,Data set,Perlin noise,Computer science,Visualization,Scalar (physics),Artificial intelligence,Scientific visualization,Computer graphics,Creative visualization
Conference
Citations 
PageRank 
References 
10
0.51
13
Authors
4
Name
Order
Citations
PageRank
Alexandre Coninx1276.95
Georges-Pierre Bonneau233632.21
Jacques Droulez312115.77
Guillaume Thibault4527.01