Introduction to scientific visualization
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Introduction to scientific visualization

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  • 1. Introduction to Data Visualization
    Hao Wang
    Thoughtworks
  • 2. Data Acquisition
  • 3. 2D Scalar Fields
  • 4. Quantitative v.s Qualitative
  • 5. Redundant Cues
  • 6. Trumbo’s Principles
    Ordered values should be represented by ordered colors
    Significantly different levels should be given significantly different colors
  • 7. Height Field
  • 8. Isocontours
  • 9. Marching Triangles
  • 10. Marching Cubes
  • 11. Marching Cubes
  • 12. Speedup
    Octree
    Extrema Graph
    View Dependent Approach
  • 13. Direct Volume Rendering
    What if we want to see multiple layers of data?
  • 14. Optical Models
  • 15. Optical Models
    Maximum Intensity Projection
    Absorption
    Absorption and Emission
  • 16. Transfer Functions
  • 17. Isosurfacev.s Volume Rendering
    Isosurface: hard, binary
    Volume Rendering: soft, noisy
  • 18. Vector Fields
  • 19. Streamlines
  • 20. LIC
  • 21. LIC
  • 22. 3D Vector Fields
  • 23. Critical Points
  • 24. Information Visualization
  • 25. Multivariate Data
  • 26. Hierarchical Data
  • 27. Hierarchical Data
  • 28. Map Data
  • 29. Social Data
  • 30. Acknowledgement
    Charles Hansen, University of Utah
    Claudio Silva, University of Utah
    Carlos Scheidegger, AT&T
    IBM Manyeyes