TNM093 Tillämpad visualisering - Linköping Universitystaffjimjo/courses/TNM093-2013/... · tillämpningar inom visualisering och datorgraﬁk • använda tracking och korrekt graﬁsk

TNM093Tillämpad visualisering och virtuell verklighet

Jimmy JohanssonC-Research, Linköping University

Tuesday, September 3, 13

Aim of Course

• använda vanliga tekniker för datautforskning och -analys, samt virtuell verklighet för att implementera tillämpningar inom visualisering och datorgrafik

• använda tracking och korrekt grafisk projektion för att implementera ett-till-ett-mappning mellan verkliga och virtuella koordinater

• använda vanliga programvaror och system för att implementera grundläggande multi-sensoriska interaktioner och visualiseringar

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Teachers

• Dr. Jimmy Johansson (Information visualization), Kursansvarig

• Prof. Timo Ropinski (Scientific visualization)

• Dr. Karljohan Palmerius (Virtual reality)

• Alexander Bock (Lab assistant)

• Jonas Strandstedt (Lab assistant)

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Course structure

• Ht1- Visualization‣ Lectures

- L1- introduction + infovis- L2 - infovis + data mining- L3 - Scivis 1- L4 - Scivis 2- lecture on Monday 23/9 8-10 will NOT

be used

‣ Visualization Lab

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Course structure

• Ht2- Virtual reality‣ Lectures

- L5- VR display systems- L6 - Navigation and interaction- L7 - Software and programming

‣ Vr Lab

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Examination

• Laboratory work, 6hp

• Examination is performed during the lab sessions

• Note! Extensive labs‣ 1 vis lab, divided into 6 parts‣ 2 VR labs‣ Total study time for all labs is 140 hours‣ Lab sessions only for getting help‣ Requires extensive self-studying

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Examination

• Laboratory work, 6hp‣ 2 lab groups, sign up on paper, floor 4 visualization

centre‣ 2 students in each group‣ Labs will be in K4504 and in the VR lab in the

visualization centre (need to sign a form for using the VR lab)

‣ First lab sessions (19/9) will give an introduction to OpenGL/GLSL (all you need for the labs)

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Course Literature

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Course webpage

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http://webstaff.itn.liu.se/~jimjo/courses/TNM093-2013


http://webstaff.itn.liu.se/~jimjo/courses/TNM093-2013/




First time this course is given

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• No previous course evaluation

• Please let me know if something is not working!!


Vis + VR

• Data representation

• Visualization methodologies

• Interaction and navigation

• Display techniques

• Psychophysics - connection between biomechanics and mental effects

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Virtual autopsy table

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Introduction to Visualization

• New Oxford Dictionary of English, 1999

• visualize - verb [with obj.]‣ Form a mental image of; imagine: it is not

easy to visualize the future.‣ Make (something) visible to the eye: the

DNA as visualized by staining with ethidium bromide.

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Visualization is...

• The process by which we understand things

• The process by which we interpret information - build our mental picture

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• This is not a visualization!

• This is a picture!‣ A representation of data

• The visualization is all in *your* mind‣ It’s personal and unique

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Visualization is...

• Creating representations of data to allow the user to: - visualize (understand) information- visualize (identify) patterns and

relationships present- Gain understanding- Acquire insight

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What the user wants

• To be presented interpretable information

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Representation Perception

CognitionInsight


Representations

• Must be concise:‣ All the information‣ As simply as possible

• Perceivable:‣ Easy to sense (see, hear, feel, smell...)

• Interpretable:‣ Provide cues which inform the user

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Why Visualization?

• Large data

• Multivariate data‣ Lots of data sources‣ Data fusion

• Time-dependent data‣ Measured or simulated

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Why visualize?

Mean x: 9Mean y: 7.5Var x: 11Var y: 4.1Corr (x,y): 0.8 LR: y = 3.0+0.5x


Representations

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Complex Simple


Perception

• Perception: the process of attaining awareness or understanding of sensory information

• The foundation of all IT systems

• Very important in visualization


Pre-attentive processing

• A limited set of visual properties that are detected very rapidly and accurately by the low-level visual system

• 200-250 milliseconds



• A limited set of visual properties that are detected very rapidly and accurately by the low-level visual system

• 200-250 milliseconds



Intersection

Curvature Hue Density

Orientation 3D


Change blindness


Change blindness


Change blindness


Inattentional blindness


Visualization Pipeline

• Series of stages from data to representation

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Raw Data

InternalData

RefinedData Objects

Data ReaderRender

MappingFilter

Interaction

Representation


VP: Filtering

• Selecting data to seek specific relationships

• Reducing data to manageable sizes

• Modifying the internal structure

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InternalData

RefinedData

Filter


VP: Mapping

• Select representation

• Map refined data to model

• Driven by models of human perception

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RefinedData Objects

Mapping


VP: Rendering

• Render (simple) shapes/forces/sounds/...

• Simple for ease of interpretation...

• ...and ease of interaction‣ Vital for exploration

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Objects

Render

Representation


Visualization methods

• 1D/2D: Graphs, charts, scatterplots, maps

• Multivariate:‣ plot matrices‣ Parallel coordinates

• Volume data:‣ Direct volume rendering‣ Geometric: isosurfacing

• Vector data: glyphs (arrows etc), streamlines

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Data Types

• Direct mapping

• Intuitive

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• No spatial mapping

• Non-intuitive

Spatial Abstract


Types of data

Categorical / Qualitative Numerical / Quantitative

Nominal Ordinal Interval Ratio

Model

Saab

Volvo

Opel

Boot

Large

Medium

Small

Year

08

03

07

Weight

1890

2020

1610


Information visualization

• Visualization of abstract data

• (Usually) no connection to any specific coordinate system

• Examples: financial data, internet logs (all types of statistics)


Encoding of value


Tabular data

Model Boot Year Weight

Saab Large 08 1890

Volvo Medium 03 2020

Opel Small 07 1610


Tabular data


Saab Large 08 1890


Opel Small 07 1610

Data value


Tabular data


Saab Large 08 1890


Opel Small 07 1610

Variable / parameter / dimension


Tabular data


Saab Large 08 1890


Opel Small 07 1610

Variable / parameter / dimension


Tabular data


Saab Large 08 1890


Opel Small 07 1610

Object / item / tuple / record


Tabular data


Saab Large 08 1890


Opel Small 07 1610

Object / item / tuple / record


The encoding of value

V1

V2

V1

V2

V1

V2 V1

V2

V1

V2

V1

Time

2D scatter plots

Pos. corr. Neg. corr. No corr.

Cluster Outlier TimeTuesday, September 3, 13


Scatter plots of ND-data

Variable1 Variable 2 Variable 3 Variable 4

X-axis Y-axis Size Colour



Scatter plot matrix


Parallel coordinates

Tax rates House price

Population Birth-rate





23 34000 2300000 27



23

34000 2300000 27





23 34000 2300000 27







23 34000 2300000 27



28 12000 1900000 25

…







Negativ korrelation

Positiv korrelation

Ingen uppenbar korrelation






Table Lens MPG Horsepower Weight Acceleration Cylinders Year


Table Lens MPG Horsepower Weight Acceleration Cylinders Year


Mosaic plot

Titanic


Mosaic plot

3rd1st 2nd Crew


Mosaic plot

3rd1st 2nd Crew

Adult

Child


Mosaic plot

3rd1st 2nd Crew

Adult

Child

Female / Male


Choice of encoding

• Bertin´s guidance


Encoding of quantitative values


3D Representations

• Use 3D wisely

• More dimensions do not mean that more information is simultaneously displayed


3D Representations

• Use 3D wisely



3D Representations

• Use 3D wisely



TNM093 Tillämpad visualisering - Linköping Universitystaffjimjo/courses/TNM093-2013/... · tillämpningar inom visualisering och datorgraﬁk • använda tracking och korrekt graﬁsk

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