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Visualization

Visualization. CSE 694L Roger Crawfis. The Ohio State University. Introduction. Getting acquainted - teams Current studies or major Hometown A interesting data problem. Outline. Scientific Visualization Data Topologies and Data Sources 1D and 2D Visualization Algorithms

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Visualization

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  1. Visualization CSE 694L Roger Crawfis The Ohio State University

  2. Introduction • Getting acquainted - teams • Current studies or major • Hometown • A interesting data problem R. Crawfis, Ohio State Univ.

  3. Outline • Scientific Visualization • Data Topologies and Data Sources • 1D and 2D Visualization Algorithms • Overview of 3D Visualization techniques • Iso-contour surfaces • Volume Rendering • Transfer functions and segmentation (2D) • Flow Visualization Algorithms (2D) R. Crawfis, Ohio State Univ.

  4. Outline (con’t) • Volume Rendering • Optical Models • Algorithms • Transfer Functions • Global Vector Field Visualization • Virtual Environments • The CAVE • Data or Information Visualization • Overview of perceptual issues • Brushing • Focus + context • Successes R. Crawfis, Ohio State Univ.

  5. What is Visualization? • Understanding of data • Insight into information • Presentation and sharing of insights. R. Crawfis, Ohio State Univ.

  6. Data Sources • Computational Science • Data Acquisition / Imaging • Historical Observation • Survey, Census, etc. R. Crawfis, Ohio State Univ.

  7. Data Topologies - Structured • Cartesian • x j+1 = xi +  • Regular or Uniform • x j+1 = xi + x • Rectilinear or Perimeter • x j+1 = x(i) R. Crawfis, Ohio State Univ.

  8. Data Topologies - Structured i=0 • Curvilinear • x j+1 = x(i,j) • y j+1 = y(i,j) • Curves may intersect in i or j • Curves may not cross in i or j i=3 i=0 j=1 R. Crawfis, Ohio State Univ.

  9. Data Topologies - Unstructured • Unstructured or cell data or finite-element data • Tetrahedral R. Crawfis, Ohio State Univ.

  10. Data Topologies - Unstructured • Hexahedral R. Crawfis, Ohio State Univ.

  11. Data Topologies - Unstructured • Finite-element zoo R. Crawfis, Ohio State Univ.

  12. New Data Topologies • Improved data topologies offer huge potential for savings in computational science • Hierarchical models are becoming more common R. Crawfis, Ohio State Univ.

  13. New Data Topologies • Hierarchical • Multi-Block Curvilinear • N-sided Polyhedron where n>6 • Multi-Grid or Adaptive Mesh Refinement R. Crawfis, Ohio State Univ.

  14. Working with data • Reconstruction is critical • Useful Image Processing operations • Histogram • Data mappers • Region of interest selection • Edge detection • Noise removal or blurring R. Crawfis, Ohio State Univ.

  15. 1D and 2D Visualization Techniques CSE 694L

  16. 1D Visualization • y = f(x) • Line Charts • Curve Fitting • Smoothing or Approximation R. Crawfis, Ohio State Univ.

  17. 1D Visualization • Non-cartesion coordinate systems R. Crawfis, Ohio State Univ.

  18. Basic 2D Visualizations • Scalar Data on a Regular Grid • Surface plots(2D graphics) R. Crawfis, Ohio State Univ.

  19. 2D Visualizations • Contour Lines - f(x,y) = constant R. Crawfis, Ohio State Univ.

  20. 2D Visualizations • Psuedo Coloring R. Crawfis, Ohio State Univ.

  21. 2D Computer Graphics • Image formats and pixel limitations • Color Tables • grey-scale • hot to cold • perceptual R. Crawfis, Ohio State Univ.

  22. Transfer Functions • Besides the basics, most tools allow you to define your own color mappings. R. Crawfis, Ohio State Univ.

  23. 2D Visualization • Vector Fields • Hedgehogs • Streamlines R. Crawfis, Ohio State Univ.

  24. 1D Visualization • Vector? R. Crawfis, Ohio State Univ.

  25. 2D Contouring • Continuous f(x,y) • Use steepest decent to find zero crossing (root) of the function f(x,y)-c • Follow contour from this seed point until we reach a boundary or loop back. • Direction close to f  z • Problems? R. Crawfis, Ohio State Univ.

  26. 2D Contouring • Discrete Data • Assume the Mean Value Thereom • Assume monoticity? • 1D Analogy • 5 Points R. Crawfis, Ohio State Univ.

  27. 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 2D Contouring • Given a quadrilateral • f(x,y) = 0.5 <0.5 >0.5 R. Crawfis, Ohio State Univ.

  28. Three-dimensional Visualization Techniques CSE 694L

  29. Visualization Algorithms • 2D Slice planes in 3D R. Crawfis, Ohio State Univ.

  30. 2D Slice Planes • Orthogonal to a coordinate axis • Uniform Grids => image • Arbitrary • Specify the normal and a point • Produces a 2D unstructured grid R. Crawfis, Ohio State Univ.

  31. 2D Slice Planes • Mesh with colors at vertices • 128x128x128 volume can produce over 50,000 triangles. • Mesh with 2D texture coordinates • Very slow if no hardware support • More precise transitions • Mesh with 3D texture coordinates R. Crawfis, Ohio State Univ.

  32. 2D Flip book of slices • Rather than view the 2D slice in 3D, rapidly play a sequence of orthogonal slice planes in a movie loop. • Sometimes difficult to build a complete mental model. R. Crawfis, Ohio State Univ.

  33. 3D Visualizations • Point plots • Animation can bring out the surface or pattern (MacSpin) • Depth Cueing aids in the depth perception. R. Crawfis, Ohio State Univ.

  34. 3D Visualizations • Spheres or cubes dispersed throughout the volume • color-coded • optional shape-controlled. R. Crawfis, Ohio State Univ.

  35. 3D Visualization • Iso-contour surfaces R. Crawfis, Ohio State Univ.

  36. 3D Visualization • Can add information about an additional variable • Here, two additional variables control the color. R. Crawfis, Ohio State Univ.

  37. 3D Visualization • Volume Rendering R. Crawfis, Ohio State Univ.

  38. 3D Visualization R. Crawfis, Ohio State Univ.

  39. Material Classification • Drebin, Carpenter and Hanrahan • Material Probabilities • Levoy • Contour surfaces • MRI data • Skin versus Brain • Using Texture mapping R. Crawfis, Ohio State Univ.

  40. Transfer Functions • Maps raw voxel value into presentable entities: color, intensity, opacity... • Raw-data  material (R, G, B, a, Ka, Kd, Ks,...). • Material  shaded material. • High gradient in the data values detects a surface and is used as a measure of its orientation. R. Crawfis, Ohio State Univ.

  41. 3D Visualization • Volume Rendering can mimic contouring. • Use a transfer function with an impulse at the contour level. R. Crawfis, Ohio State Univ.

  42. What makes a good visualization? • Why are some things or fields portrayed the way they are? • Take map making for instance. • Which of the following is “better”? R. Crawfis, Ohio State Univ.

  43. Different map projections R. Crawfis, Ohio State Univ.

  44. R. Crawfis, Ohio State Univ.

  45. Different map projections R. Crawfis, Ohio State Univ.

  46. Different map projections R. Crawfis, Ohio State Univ.

  47. Different map projections R. Crawfis, Ohio State Univ.

  48. Different map projections R. Crawfis, Ohio State Univ.

  49. Different map projections R. Crawfis, Ohio State Univ.

  50. R. Crawfis, Ohio State Univ.

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