Visualizing Uncertainty in Volume Rendering

1. Visualizing Uncertainty in Volume Rendering Suzana Djurcilov*, Kwansik Kim*, Pierre Lermusiaux† and Alex Pang* * UC Santa Cruz † Harvard University

2. OVERVIEW Introduction Inline Approach Post-Processing Approach Future Directions

3. 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

4. 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

5. Inline Approach • Direct Volume Rendering (DVR) • 1D Transfer Function • Opacity mapping of uncertainties • 2D Transfer function

6. Inline Approach Direct Volume Rendering (DVR) Example visualization of Salinity data using DVR : opacity C: color intensity E: emission

7. 1D Transfer function • Thresholding • Map uncertainty to opacity • Leave color transfer intact • High uncertainty areas more noticeable

8. 1D Transfer Function : uncertainty thresholding DVR of uncertainty > 0.2 DVR of uncertainty > 0.5

9. 1D Transfer Function : mapping uncertainties to opacity values Transfer function Increasing opacity with uncertainty salinity temperature

10. 1D Transfer Function : higher contrast Transfer function Increasing opacity with uncertainty temperature salinity

11. 2D Transfer function - Histogram • Create a graph of data vs. uncertainty • Map different regions to different colors • Override the transfer function

12. 2D Transfer Function : histogram

13. 2D Transfer Functions 2D transfer function Salinity data

14. 2D Transfer Functions 2D transfer function Salinity data

15. 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

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17. 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

18. Variable hole size 1 pixel 4 pixels

19. Speckle intensity • Higher uncertainty --> darker hole • Gray-scale color • Vary both density and shade of hole

20. 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

21. 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

22. Texture Examples

23. Adding Noise • Change the DVR image directly • Alter pixels in areas of high uncertainty • Distance in color space proportional to uncertainty

24. Noise Example

25. Future Work • Extend the application domain • Incorporate depth information into post-processing • Non-scalar (range, distribution) uncertainty

26. 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