Visualisatie BMT Introduction, visualization, visualization - PowerPoint PPT Presentation

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 • Visualization pipeline 2

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

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

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

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

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

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

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

Visualization 10

What do we visualize? 11

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

Visualization - insight in data 13

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

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

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

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

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

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

Some examples 20

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

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

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

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

Applications 25

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

Molecular visualization 27

Molecular visualization 28

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

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

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

Medicine 32

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

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

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

Space sciences Orion Nebula as seen from a virtual spacecraft 36

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

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

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

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

Architecture Simulation of light in a theatre 41

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

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

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

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

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

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

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

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

Example 50

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

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

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

Visualization and research process 54

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