Ivan Viola Vienna University of Technology Austria

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Declarative Visualization Ivan Viola Vienna University of Technology Austria

Transcript of Ivan Viola Vienna University of Technology Austria

Declarative Visualization

Ivan ViolaVienna University of Technology

Austria

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● Aligned with the data-flow network● Data is “thrown over fence” on visualizers● Piped into visual representation● Splatted on to the display● Viewer is staring at it

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Traditional Visualization Pipeline

Data-Centric Stage Computation-Centric Stage

User-Centric Stage

Acquisition Filtering Visual Mapping Rendering Display

Optical TransferViewingPerceptionCognition

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Direct Volume Rendering● Imperative

character● Multitude of

parameters to adjust(which could be automatized)

● Effect of parameter change is hard to predict

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Visualization Goal● Visualization is enabling technology● Primary goal is to provide insight● Exploiting perceptual / cognitive capabilities ● Specific tasks to reach the goal● Strictly generic pipeline does not exist● Common pattern: visual dialog: HMD● Data: measurements, models, mental reps.● Goal is the reason for visualization

● Imperative paradigm: Splat data on the user● Declarative paradigm:

User drives visualization of data

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Information Flow

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Data-Centric Stage Computation-Centric Stage

User-Centric Stage

Acquisition Filtering Visual Mapping Rendering Display

Optical TransferViewingPerceptionCognition

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Pipeline PatternsMOVADDCMP

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Importance-Driven Volume Rendering

Howell Medigraphics

[Viola et al. 2004]

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Design Guidelines for Geo-Cutaways

[Lidal et al. 2012]

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Importance-Driven Focus of Attention

[Viola et al. 2006]

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Importance-Driven Focus of Attention

v 1

v 2

v 3

o 1

o 2

o 3

visibility estimation image-space weight

p(v 1)

p(v n)

p(o 1 |v 1)

p(o m |v n)

p(o 1) p(o m)

...

I(v i ,O) = p(o j |v i ) log∑j

m p(o j |v i)p(o j)

...

...

information-theoretic framework for optimal viewpoint estimation

v

o 1

o 2

o 3

object-space distance weight

...

[Viola et al. 2006]

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Occlusion Shading

[Solteszova et al. 2011]

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● Constant perceptual distancebetween shadow colorand object color

S3 S4 S5

S0 S1 S2

[Solteszova et al. 2011]

Chromatic Shadows

Assessment of Surface Perception● Gauge figure task [Koenderink et al. ‘92]

Ivan Viola 16 [Solteszova et al. 2011]

Experiment on Surface Perception● Users rotated the

gauge until it was perceived tangential to the surface

● Perceived and ground truth normal

● Tested shadow colors S0-S4 from the palette

Ivan Viola 17 [Solteszova et al. 2011]

Experiment on Depth Perception● Relative depth estimation of a yellow point

with respect to the red and blue point

Ivan Viola 18 [Solteszova et al. 2011]

User-Centric Stage

Data-Centric Stage Computation-Centric Stage

Acquisition Filtering Visual Mapping Rendering Display

Optical TransferViewingPerceptionCognition

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● Final visual design is at the end evaluated● The outcome can be…

● positive or● negative…

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Traditional Role of Evaluation

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initial transfer function

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Importance-Driven Visibility

rind = 0.25; pulp = 0.6; seeds = 0.15

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[Viola 2005]

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● The goal is to provide most accurate match between information and its perceptual stimulus

● Iterative approach of visualization redesign

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Iterative Visualization Redesign

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Perceptual-Statistics Shading Model

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[Solteszova et al. 2012]

Surface Slant and Tilt

Ivan Viola 25 [Solteszova et al. 2012]

Underestimation of Slant

[Stu

dy D

ata:

Col

e et

al.]

GROUND TRUTH SLANT

ESTI

MAT

ED S

LAN

T

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Optimized Normal-Based Shading

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Quantifying the Error

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Quantifying the Error

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Iterative Evaluation and Redesign

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● How is motion perceived in relation of one to another?

● Can we linearize perception of motion?

● Estimation from a motion legend

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Perceptually Uniform Motion

[Birkeland et al. 201X]

● Task: Estimate relative speed-up factor● Global scale of velocities● Direction● Contrast-type● Representation

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Studied Characteristics

[Birkeland et al. 201X]

● Task: Estimate relative speed-up factor● Global scale of velocities● Direction● Contrast-type● Representation

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Studied Characteristics

[Birkeland et al. 201X]

● Task: Estimate relative speed-up factor● Global scale of velocities● Direction● Contrast-type● Representation

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Studied Characteristics

[Birkeland et al. 201X]

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Information Visualization: Circle Size

● Automation and regulation systems are based on a feedback loop mechanism

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Declarative Visualization Workflow

Measurederror

SysteminputReference System output

Measured output

+

Controller System

Sensor

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● Can we learn from process automation?● What would the PID controller look like?

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Declarative Visualization Workflow

InputMeasured

error

Systeminput

Reference

System output

Measuredoutput

+

Controller

Acquisition

Filtering

System – Visual Interface

Visual Mapping Rendering

DisplayOptical Transfer

Viewing Perception

Cognition

Sensor – Study

Task

Analysis

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Measurederror

SysteminputReference System output

Measured output

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Controller System

Sensor

● Psychophysics● Controlled Study● Surveillance

● Eye Tracking (Tobii)● Digital Pen (Lifetrons)● EEG (Emotiv)

● Crowdsourcing● Statistical Analysis● Individuality is realityIvan Viola 38

Sensor

● ReCaptcha idea

● ReGauge-figure task?

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Invisible Perceptual Study

overlooks inquiry

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Statistical Model of Illustration

Eye Tracking

Pen Tracking

EEG Tracking

Surveillance

Reference Scene

IllustrationCrafting

Reference Geometry

Surveillance Data

Statistical model

InputMeasured

error

Systeminput

Reference

System output

Measuredoutput

+

Controller

Acquisition

Filtering

System – Visual Interface

Visual Mapping Rendering

DisplayOptical Transfer

Viewing Perception

Cognition

Sensor – Study

Task

Analysis

Analysis

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Pipeline Patterns

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● Veronika Šoltészová● Åsmund Birkeland● Endre Lidal● Manu Waldner● many others!

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Thanks