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Visualisatie BMT Introduction, visualization, visualization pipeline Arjan Kok Huub van de Wetering (h.v.d.wetering@tue.nl) 1 Lecture overview Goal Summary Study material What is visualization Examples

  1. Visualisatie BMT Introduction, visualization, visualization pipeline Arjan Kok Huub van de Wetering (h.v.d.wetering@tue.nl) 1

  2. Lecture overview • Goal • Summary • Study material • What is visualization • Examples • Visualization pipeline 2

  3. Goal • Provide theoretical and practical knowledge in: • Data visualization • Data representation • Computer graphics • Data processing in Java • Visualization in MayaVi 3

  4. Summary (1) • Introduction • What is visualization • Related disciplines • Fields of applications • The visualization pipeline • Definition • Data enrichment, mapping, rendering 4

  5. Summary (2) • Basic data representation • Datasets • Sampling • Interpolation • Graphics rendering • Rendering process • Color • Lighting, shading 5

  6. Summary (3) • Algorithms • Scalar algorithms • Vector algorithms • Tensor algorithms • Modeling algorithms • Volume visualization • Ray tracing, ray sampling • Volume interpolation • … 6

  7. Study material • Theory • Book • Slides • Practice • MayaVi (visualization tool) • Jaspis (java programming tool) • Assignments 7

  8. Book • The Visualization Toolkit –An Object-Oriented Approach to 3D Graphics W. Schroeder, K. Martin, B. Lorensen Prentice Hall • Book contains a lot more than the course does (course will address specific parts/chapters) • Book contains software (VTK) we shall not (directly) use 8

  9. Slides • Slides used in lectures will be available at: http://www.win.tue.nl/~wstahw/2Z860 9

  10. Visualization 10

  11. What do we visualize? 11

  12. Visualization The purpose of computing is insight, not numbers - Richard Hamming 12

  13. Visualization - insight in data 13

  14. From data to pictures • Attributes of Visualization • Making abstract data visible (complex, many) • Forming a mental image of something abstract • Using the abilities of human vision and interaction DATA PICTURES VISUALIZATION 12.4556 34.442 -22.2000E+11 0.3324 a: 27.3099 b: 43.3 C:33.323 34.445 14

  15. Scientific visualization • The use of computer imaging techniques as a tool for comprehending data obtained by simulation or physical measurements • The techniques that allow scientists and engineers to extract knowledge from the results of simulations and computations 15

  16. Goals in visualization • Exploration of data and information • Enhancing understanding of concepts and processes • Gaining new (unexpected) insight • Making invisible visible • Effective presentation of significant features • Quality control of simulations and measurements • Increasing scientific production 16

  17. Visualization challenges • Getting usable data • Parsable • Visualizable • Defining your goal • What is the focus of attention or primary features • Who is the audience • What is the message • Choosing meaningful/compelling visual representations 17

  18. Graphs 20 18 16 14 12 10 8 6 4 2 0 1 2 3 4 5 6 7 8 9 10 18

  19. Complex data • We are interested in more complex data • Multi-dimensional • Complex geometry • Computed or collected • Simulations • MRI, CT, .. • Microscopic to galactic data collections 19

  20. Some examples 20

  21. Related disciplines USER INTERFACE STUDIES V IMAGE GEOMETRIC I PROCESSING MODELING S U A PERCEPTUAL COMPUTER L PSYCHOLOGY GRAPHICS I Z A COMPUTER SIGNAL T AIDED DESIGN PROCESSING I O N 21

  22. Imaging, graphics, visualization • Imaging • The study of 2D images (transformations, enhancement, information extraction) • Graphics • Creating images using a computer (2D drawing techniques, 3D rendering techniques) • Visualization • Exploring, transforming, and viewing data as images 22

  23. Imaging, graphics, visualization • Visualization uses computer graphics and imaging as tools for the higher level goal of getting insight into data • Graphics and imaging are particular forms of visualization … 23

  24. Imaging, graphics, visualization Imaging Graphics Visualization Data 2D 2D, 3D nD dimensionality image 2D/3D object any data Data transformation image image image 24

  25. Applications 25

  26. Applications • Biochemistry • Molecular modeling/dynamics • Industrial research on molecular structures • Drug design DATA PICTURES VISUALIZATION molecule structures 26

  27. Molecular visualization 27

  28. Molecular visualization 28

  29. Applications • Mathematics • Understanding complex concepts (functions, surfaces, fields, ..) DATA PICTURES VISUALIZATION functions f(x,y,z) function plot 29

  30. Mathematics z = F(x,y) = e -r cos(10r ) nested implicit functions saddle quadric surface F(x,y,z) = 0 30

  31. Applications • Medicine • Diagnosis • Treatment planning • Education • Research DATA PICTURES VISUALIZATION 2D/3D scan data surfaces/ slices 31

  32. Medicine 32

  33. Examples • Geosciences • Weather forecast • Topography • Geology DATA PICTURES VISUALIZATION surface/ volume data surfaces/ height plots 33

  34. Geosciences Rain during summer 2004 Ocean surface height during the El Nino event 34

  35. Applications • Space sciences • Astronomy • Astrophysics • Remote sensing 35

  36. Space sciences Orion Nebula as seen from a virtual spacecraft 36

  37. Applications • Engineering and physics • Computational fluid dynamics • Fluid flow simulation • Surface modeling • Finite element simulations • Physical processes (strength, elasticity, flow, ..) 37

  38. Computational fluid dynamics velocity of a turbulent jet flow air pressure on a plane wing internal waves inside the ocean 38

  39. Finite element methods pressure on a plane wing 2D flow past a cylinder 39

  40. Applications • Architecture • Simulations of: • Indoor lighting • Sound • Heath • Air 40

  41. Architecture Simulation of light in a theatre 41

  42. Applications • Visualization is applicable in any research or engineering field DATA PICTURES VISUALIZATION 12.4556 34.442 -22.2000E+11 0.3324 a: 27.3099 b: 43.3 C:33.323 34.445 42

  43. Visualization pipeline • Describes the steps to transform “raw” data into displayable images • Goal of these steps is to convert the information to a format amenable to understanding by the human perceptual system while maintaining the integrity of information 43

  44. Visualization pipeline Raw Data Data Enrichment/Enhancement Derived Data Visualization Mapping Abstract Visualization Object Rendering Displayable Image 44

  45. Getting the data Measured data Simulation data Data formats Data compression my own format HDF, NetCDF, XDR, RLE, Fractal methods, Dicom, …. …. Visualization internal data (ready for the pipeline) 45

  46. Step 1: Data enrichment • Data enrichment • Interpolation • Filtering and smoothing • Selection • Merging • Format conversion • 2D and 3D conversions (rotation, translation) data enrichment data object(s) data object(s) (filter object) 46

  47. Step 2: Mapping • Mapping • Generating displayable data (2D and 3D objects) whose shape, dimensions and color represent the enriched data • Abstract visualization objects • The 2D and 3D objects resulting from the mapping stage (graphical primitives) mapping abstract data object(s) (mapper object) visualization objects 47

  48. Step 3: Rendering • Rendering • Produces an image (view) of the 2D/3D abstract visualization objects • Several rendering parameters (lighting, shadows, reflections, etc) abstract rendering image(s) visualization objects 48

  49. Step 3: Rendering • Rendering • Special rendering techniques such as volume rendering for non-opaque data volume data object(s) image(s) rendering 49

  50. Example 50

  51. Example pipeline reader outline data mapper lines filter polydata data mapper surfaces render str. pnts data geometry mapper surface polydata filter image 51

  52. Visualization and interaction Raw Data Data Enrichment/Enhancement u s e Derived Data r Visualization Mapping i n Abstract Visualization Object p u t Rendering Displayable Image 52

  53. Visualization and research process • Visualization plays a large role in forming the link between hypothesis and experiment , and between insight and new hypothesis 53

  54. Visualization and research process 54

  55. Visualization pipeline (revisited) Raw Data Data Enrichment/Enhancement Derived Data Visualization Mapping Abstract Visualization Object Rendering Displayable Image 55

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