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Lecture 14: Scientific Visualization Information Visualization CPSC 533C, Fall 2006

Lecture 14: Scientific Visualization Information Visualization CPSC 533C, Fall 2006. Tamara Munzner UBC Computer Science 26 Oct 2006. Credits. almost unchanged from lecture by Melanie Tory (University of Victoria) who in turn used resources from Torsten Möller (Simon Fraser University)

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Lecture 14: Scientific Visualization Information Visualization CPSC 533C, Fall 2006

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  1. Lecture 14: Scientific VisualizationInformation VisualizationCPSC 533C, Fall 2006 Tamara Munzner UBC Computer Science 26 Oct 2006

  2. Credits • almost unchanged from lecture by Melanie Tory (University of Victoria) • who in turn used resources from • Torsten Möller (Simon Fraser University) • Raghu Machiraju (Ohio State University) • Klaus Mueller (SUNY Stony Brook)

  3. News • Reminder: no class next week • I'm at InfoVis/Vis in Baltimore

  4. Overview • What is SciVis? • Data & Applications • Iso-surfaces • Direct Volume Rendering • Vector Visualization • Challenges

  5. SciVis InfoVis Difference between SciVis and InfoVis Parallel Coordinates Direct Volume Rendering [Hauser et al.,Vis 2000] [Fua et al., Vis 1999] Isosurfaces Glyphs Scatter Plots Line Integral Convolution [http://www.axon.com/gn_Acuity.html] Node-link Diagrams [Cabral & Leedom,SIGGRAPH 1993] Streamlines [Lamping et al., CHI 1995] [Verma et al.,Vis 2000]

  6. Difference between SciVis and InfoVis • Card, Mackinlay, & Shneiderman: • SciVis: Scientific, physically based • InfoVis: Abstract • Munzner: • SciVis: Spatial layout given • InfoVis: Spatial layout chosen • Tory & Möller: • SciVis: Spatial layout given + Continuous • InfoVis: Spatial layout chosen + Discrete • Everything else -- ?

  7. Overview • What is SciVis? • Data & Applications • Iso-surfaces • Direct Volume Rendering • Vector Visualization • Challenges

  8. Medical Scanning • MRI, CT, SPECT, PET, ultrasound

  9. Medical Scanning - Applications • Medical education for anatomy, surgery, etc. • Illustration of medical procedures to the patient

  10. Medical Scanning - Applications • Surgical simulation for treatment planning • Tele-medicine • Inter-operative visualization in brain surgery, biopsies, etc.

  11. Biological Scanning • Scanners: Biological scanners, electronic microscopes, confocal microscopes • Apps – physiology, paleontology, microscopic analysis…

  12. Industrial Scanning • Planning (e.g., log scanning) • Quality control • Security (e.g. airport scanners)

  13. Scientific Computation - Domain • Mathematical analysis • ODE/PDE (ordinary and partialdifferential equations) • Finite element analysis (FE) • Supercomputer simulations

  14. Scientific Computation - Apps • Flow Visualization

  15. Overview • What is SciVis? • Data & Applications • Iso-surfaces • Direct Volume Rendering • Vector Visualization • Challenges

  16. Isosurfaces - Examples Isolines Isosurfaces

  17. Isosurface Extraction 0 1 1 3 2 • by contouring • closed contours • continuous • determined by iso-value • several methods • marching cubes is most common 1 3 6 6 3 3 7 9 7 3 2 7 8 6 2 1 2 3 4 3 Iso-value = 5

  18. MC 1: Create a Cube • Consider a Cube defined by eight data values: (i,j+1,k+1) (i+1,j+1,k+1) (i,j,k+1) (i+1,j,k+1) (i,j+1,k) (i+1,j+1,k) (i,j,k) (i+1,j,k)

  19. MC 2: Classify Each Voxel • Classify each voxel according to whether it liesoutside the surface (value > iso-surface value)inside the surface (value <= iso-surface value) 10 10 Iso=9 5 5 10 8 Iso=7 8 8 =inside =outside

  20. MC 3: Build An Index • Use the binary labeling of each voxel to create an index v8 v7 11110100 inside =1 v4 outside=0 v3 v5 v6 00110000 v1 v2 Index: v3 v5 v6 v1 v2 v4 v7 v8

  21. MC 4: Lookup Edge List • For a given index, access an array storing a list of edges • all 256 cases can be derived from 15 base cases

  22. MC 4: Example • Index = 00000001 • triangle 1 = a, b, c c a b

  23. MC 5: Interp. Triangle Vertex • For each triangle edge, find the vertex location along the edge using linear interpolation of the voxel values i+1 i x =10 =0 T=8 T=5

  24. MC 6: Compute Normals • Calculate the normal at each cube vertex • Use linear interpolation to compute the polygon vertex normal

  25. MC 7: Render!

  26. Overview • What is SciVis? • Data & Applications • Iso-surfaces • Direct Volume Rendering • Vector Visualization • Challenges

  27. Direct Volume Rendering Examples

  28. Rendering Pipeline (RP) Classify

  29. Classification • original data set has application specific values (temperature, velocity, proton density, etc.) • assign these to color/opacity values to make sense of data • achieved through transfer functions

  30. a(f) RGB(f) Shading, Compositing… Human Tooth CT Transfer Functions (TF’s) RGB a • Simple (usual) case: Map data value f to color and opacity f Gordon Kindlmann

  31. TF’s • Setting transfer functions is difficult, unintuitive, and slow a a f f a a f f Gordon Kindlmann

  32. Transfer Function Challenges • Better interfaces: • Make space of TFs less confusing • Remove excess “flexibility” • Provide guidance • Automatic / semi-automatic transfer function generation • Typically highlight boundaries Gordon Kindlmann

  33. Rendering Pipeline (RP) Classify Shade

  34. Light Effects • Usually only considering reflected part Light reflected specular Light absorbed ambient diffuse transmitted Light=refl.+absorbed+trans. Light=ambient+diffuse+specular

  35. Rendering Pipeline (RP) Classify Shade Interpolate

  36. 1D • Given: • Needed: • Needed: Interpolation • Given: 2D

  37. Interpolation • Very important; regardless of algorithm • Expensive => done very often for one image • Requirements for good reconstruction • performance • stability of the numerical algorithm • accuracy Linear Nearest neighbor

  38. Rendering Pipeline (RP) Classify Shade Interpolate Composite

  39. Ray Traversal Schemes Intensity Max Average Accumulate First Depth

  40. Ray Traversal - First • First: extracts iso-surfaces (again!)done by Tuy&Tuy ’84 Intensity First Depth

  41. Ray Traversal - Average • Average: produces basically an X-ray picture Intensity Average Depth

  42. Ray Traversal - MIP • Max: Maximum Intensity Projectionused for Magnetic Resonance Angiogram Intensity Max Depth

  43. Ray Traversal - Accumulate • Accumulate: make transparent layers visible!Levoy ‘88 Intensity Accumulate Depth

  44. 1.0 Volumetric Ray Integration color opacity object (color, opacity)

  45. Overview • What is SciVis? • Data & Applications • Iso-surfaces • Direct Volume Rendering • Vector Visualization • Challenges

  46. Flow Visualization • Traditionally – Experimental Flow Vis • Now – Computational Simulation • Typical Applications: • Study physics of fluid flow • Design aerodynamic objects

  47. Traditional Flow Experiments

  48. Techniques Glyphs (arrows) Contours Jean M. Favre Streamlines

  49. Techniques

  50. Techniques - Stream-ribbon • Trace one streamline and a constant size vector with it • Allows you to see places where flow twists

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