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Visualizing Uncertainty in Volume Rendering. Suzana Djurcilov * , Kwansik Kim * , Pierre Lermusiaux † and Alex Pang *. * UC Santa Cruz † Harvard University. OVERVIEW. Introduction Inline Approach Post-Processing Approach Future Directions. Uncertainty in Volume Rendering.
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Visualizing Uncertainty in Volume Rendering Suzana Djurcilov*, Kwansik Kim*, Pierre Lermusiaux† and Alex Pang* * UC Santa Cruz † Harvard University
OVERVIEW Introduction Inline Approach Post-Processing Approach Future Directions
Uncertainty in Volume Rendering • Volume Rendering is a single value method • Need to add a second parameter without diminishing the output of the volume rendered image • Want a task-specific visualization
Application Domain • Ocean Model (Mid-atlantic) from Harvard • Focus: temperature and salinity along the shelf-break • Uncertainty is the standard deviation over several time steps
Inline Approach • Direct Volume Rendering (DVR) • 1D Transfer Function • Opacity mapping of uncertainties • 2D Transfer function
Inline Approach Direct Volume Rendering (DVR) Example visualization of Salinity data using DVR : opacity C: color intensity E: emission
1D Transfer function • Thresholding • Map uncertainty to opacity • Leave color transfer intact • High uncertainty areas more noticeable
1D Transfer Function : uncertainty thresholding DVR of uncertainty > 0.2 DVR of uncertainty > 0.5
1D Transfer Function : mapping uncertainties to opacity values Transfer function Increasing opacity with uncertainty salinity temperature
1D Transfer Function : higher contrast Transfer function Increasing opacity with uncertainty temperature salinity
2D Transfer function - Histogram • Create a graph of data vs. uncertainty • Map different regions to different colors • Override the transfer function
2D Transfer Functions 2D transfer function Salinity data
2D Transfer Functions 2D transfer function Salinity data
Post-processing approach • Get a separate volume rendering of the primary data value and of uncertainty • Combine the two renderings into a single image • Primary value still discernible
Color background is preserved • Multi-variable representation specific to uncertainty • Holes can be larger if needed • Hole color can not be part of the transfer function
Variable hole size 1 pixel 4 pixels
Speckle intensity • Higher uncertainty --> darker hole • Gray-scale color • Vary both density and shade of hole
Using Texture • Rough textures naturally convey uncertainty • Random elements introduced into the image • Textures can be from nature (sandstone, gravel) or procedurally created • Higher contrast -> higher uncertainty
2D textures • Create textures for 5 different uncertainty levels • Quantize uncertainty and map to different texture levels • Blend the texture with the original DVR • Shade the original pixel color according to the matching texture
Adding Noise • Change the DVR image directly • Alter pixels in areas of high uncertainty • Distance in color space proportional to uncertainty
Future Work • Extend the application domain • Incorporate depth information into post-processing • Non-scalar (range, distribution) uncertainty
Acknowledgements • ONR N00014-00-1-0764 and N00014-00-1-0771 • NASA NCC2-5281 • DOE W-7405-ENG-48 • NSF ACI-9908881 • DARPA grant N66001-97-8900