computer graphics 2m370wstahw/edu/2z860/slides/vis01.pdf · • attributes of visualization •...

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VisualisatieBMT

Introduction, visualization, visualization pipeline

Arjan Kok

Huub van de Wetering ([email protected])

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Lecture overview

• Goal• Summary• Study material

• What is visualization

• Examples• Visualization pipeline

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Goal

• Provide theoretical and practical knowledge in:• Data visualization • Data representation• Computer graphics

• Data processing in Java• Visualization in MayaVi

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Summary (1)

• Introduction• What is visualization• Related disciplines• Fields of applications

• The visualization pipeline

• Definition• Data enrichment, mapping, rendering

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Summary (2)

• Basic data representation• Datasets• Sampling• Interpolation

• Graphics rendering

• Rendering process• Color• Lighting, shading

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Summary (3)

• Algorithms• Scalar algorithms• Vector algorithms• Tensor algorithms• Modeling algorithms

• Volume visualization• Ray tracing, ray sampling• Volume interpolation

• …

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Study material

• Theory• Book• Slides

• Practice

• MayaVi (visualization tool)• Jaspis (java programming tool)• Assignments

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Book

• The Visualization Toolkit –An Object-Oriented Approach to 3D GraphicsW. Schroeder, K. Martin, B. LorensenPrentice Hall

• Book contains a lot more than thecourse does (course will addressspecific parts/chapters)

• Book contains software (VTK) we shallnot (directly) use

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Slides

• Slides used in lectures will be available at:

http://www.win.tue.nl/~wstahw/2Z860

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Visualization

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What do we visualize?

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Visualization

The purpose of computing is insight, not numbers

- Richard Hamming

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Visualization - insight in data

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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 VISUALIZATION PICTURES

12.4556 34.442

-22.2000E+11 0.3324

a: 27.3099 b: 43.3

C:33.323 34.445

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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

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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

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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

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Graphs

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2

4

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1 2 3 4 5 6 7 8 9 10

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Complex data

• We are interested in more complex data• Multi-dimensional• Complex geometry• Computed or collected

• Simulations

• MRI, CT, ..• Microscopic to galactic data collections

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Some examples

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Related disciplines

SIGNAL PROCESSING

COMPUTER GRAPHICS

COMPUTER AIDED DESIGN

PERCEPTUAL PSYCHOLOGY

GEOMETRIC MODELING

IMAGE PROCESSING

VISUALIZATION

USER INTERFACE STUDIES

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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

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• 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 …

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any data

image

2D/3D object

image

image

image

Data transformation

nD2D, 3D2DData dimensionality

VisualizationGraphicsImaging

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Applications

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Applications

• Biochemistry• Molecular modeling/dynamics• Industrial research on molecular structures• Drug design


molecule

structures

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Molecular visualization

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Molecular visualization

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Applications

• Mathematics• Understanding complex concepts

(functions, surfaces, fields, ..)


functions

f(x,y,z)

function plot

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Mathematics

z = F(x,y) = e-rcos(10r)

saddle quadric surfaceF(x,y,z) = 0

nested implicit functions

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Applications

• Medicine• Diagnosis• Treatment planning• Education• Research


2D/3Dscan data

surfaces/slices

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Medicine

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Examples

• Geosciences• Weather forecast• Topography• Geology


surface/volume data

surfaces/height plots

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Geosciences

Ocean surface height during the El Nino event

Rain during summer 2004

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Applications

• Space sciences• Astronomy• Astrophysics• Remote sensing

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Space sciences

Orion Nebula as seen from a virtual spacecraft

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Applications

• Engineering and physics• Computational fluid dynamics

• Fluid flow simulation• Surface modeling

• Finite element simulations

• Physical processes(strength, elasticity, flow, ..)

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Computational fluid dynamics

air pressure ona plane wing

velocity of a turbulent jet flow

internal waves inside the ocean

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Finite element methods

pressure on a plane wing

2D flow past a cylinder

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Applications

• Architecture• Simulations of:

• Indoor lighting• Sound• Heath

• Air

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Architecture

Simulation of light in a theatre

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Applications

• Visualization is applicable in any research or engineering field


12.4556 34.442

-22.2000E+11 0.3324

a: 27.3099 b: 43.3

C:33.323 34.445

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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

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Visualization pipeline

Raw Data

Derived Data

Abstract Visualization Object

Displayable Image

Data Enrichment/Enhancement

Visualization Mapping

Rendering

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Getting the data

Simulation dataMeasured data

Data formats

HDF, NetCDF, XDR,Dicom, ….

Data compression

RLE, Fractal methods, ….

my own format

Visualization internal data(ready for the pipeline)

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Step 1: Data enrichment

• Data enrichment• Interpolation• Filtering and smoothing• Selection• Merging

• Format conversion• 2D and 3D conversions (rotation, translation)

data object(s)data enrichment

(filter object)data object(s)

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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)

data object(s)mapping

(mapper object)abstract

visualization objects

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Step 3: Rendering

• Rendering• Produces an image (view) of the 2D/3D abstract

visualization objects• Several rendering parameters

(lighting, shadows, reflections, etc)

abstract visualization objects

renderingimage(s)

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Step 3: Rendering

• Rendering• Special rendering techniques such as volume rendering

for non-opaque data

data object(s)volume

renderingimage(s)

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Example

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Example pipeline

surfacedatapolydata

datapolydata

datastr. pnts

surfaces

lines

image

outlinefilter

geometryfilter

reader

render

mapper

mapper

mapper

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Visualization and interaction

Raw Data

Derived Data


Displayable Image



Rendering

user input

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Visualization and research process

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

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Visualization and research process

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Visualization pipeline (revisited)

Raw Data

Derived Data


Displayable Image



Rendering

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