Adjust, Just Adjust Eduard Grller Institute of Visual Computing & - - PowerPoint PPT Presentation

▶
adjust just adjust
SMART_READER_LITE
LIVE PREVIEW

Adjust, Just Adjust Eduard Grller Institute of Visual Computing & - - PowerPoint PPT Presentation

Jul 16, 2023 343 likes •615 views

Adjust, Just Adjust Eduard Grller Institute of Visual Computing & Human Centered Technology TU Wien, Austria Dagstuhl 2007 Eduard Grller 2 Action without Interaction Autonomous Vehicles [1] [2] Artificial Intelligence (machine

slide-1
SLIDE 1

Eduard Gröller

Institute of Visual Computing & Human‐Centered Technology TU Wien, Austria

Adjust, Just Adjust

slide-2
SLIDE 2

Dagstuhl 2007

Eduard Gröller 2

slide-3
SLIDE 3

Action without Interaction

Autonomous Vehicles Artificial Intelligence (machine learning, deep learning, CNNs, …)

Eduard Gröller 3

[1] [2] [3]

slide-4
SLIDE 4

Chameleon Dynamic Color Mapping for Multi‐Scale Structural Biology Models

  • N. Waldin, M. Le Muzic, M. Waldner, E. Gröller,
  • D. Goodsell, A. Ludovic, I. Viola
slide-5
SLIDE 5

Multi‐Scale Structural Biology Models

Nicholas Waldin

slide-6
SLIDE 6

Multi‐Scale Structural Biology Models

Nicholas Waldin

slide-7
SLIDE 7

Chameleon: Overview

Initial color assignment View‐dependent color adjustments Hierarchical color

Nicholas Waldin

zoom

slide-8
SLIDE 8

Chameleon

Nicholas Waldin

slide-9
SLIDE 9

Output‐Sensitive Interaction

Peter Mindek, Gabriel Mistelbauer, Eduard Gröller, Stefan Bruckner

TU Wien, Austria University of Magdeburg, Germany VRVis Research Center, Austria University of Bergen, Norway

slide-10
SLIDE 10

Output‐Sensitive Interaction

Input (I), Interaction (ΔI), Paramters (P), Output (O), Transformation (T)

Peter Mindek, Eduard Gröller 10

I  P  O ΔI  ΔP  ΔO / ΔI  ΔO /  P = T(I)  ΔI  ΔO

slide-11
SLIDE 11

Mapping of the Slider to the Image Stack Positon

Eduard Gröller, Peter Mindek 11

slide-12
SLIDE 12
slide-13
SLIDE 13
slide-14
SLIDE 14

ArcBall: Data‐Sensitive Guidance (3D Rotation)

Peter Mindek, Eduard Gröller 14

slide-15
SLIDE 15
slide-16
SLIDE 16

Output‐sensitive Interaction: Summary

Output‐sensitive Interaction Data‐sensitive navigation

Manipulation Guidance

Peter Mindek, Eduard Gröller 16

ΔI  ΔO

slide-17
SLIDE 17

Visualization of 4D Ultrasound Data HDlive – together with Kretztechnik (GE) Live Fetascopic Rendering

Eduard Gröller 17

[Stefan Bruckner] [Varchola et al. 2012] [Karimov et al. 2016] [6]

slide-18
SLIDE 18

Eduard Gröller 18

slide-19
SLIDE 19

Visualizations for Broad Audiences ‐ Lessons Learned

Interaction constrained by

Hardware/software Hardware design with a few available knobs

Minimize interaction to pre‐sets

Few view positions Few lighting stages

Heterogeneous audience

Doctors do diagnosis Grandparents enjoy images

Aesthetics important, more than realism

Eduard Gröller 19

[6] [7]

slide-20
SLIDE 20

Visualizations for Broad Audiences ‐ Lessons Learned

System must be

Interactive Robust (graceful degradation)

No [costly] pre‐processing possible Engineering effort

Just good enough solution Not 100% realistic, not 100% correct

Glanceable visualization, graspable interaction, small learning effort Adhere to established preconceptions

Users got accustomed to a fast, heuristic model Acceptance of more elaborate model difficult

Eduard Gröller 20

[6] [7]

slide-21
SLIDE 21

Eduard Gröller 21

slide-22
SLIDE 22

Developing Visualizations for Broad Audiences

Automatic, context‐aware adaptation

  • f visual/interaction channels

Simple, intuitive output‐sensitive interaction Glanceable visualization, graspable interaction

Eduard Gröller 22

ΔI  ΔO

slide-23
SLIDE 23

Acknowledgements Ludovic Autin Stefan Bruckner David Goodsell Alexey Karimov Mathieu Le Muzic Peter Mindek Gabriel Mistelbauer Gerald Schröcker Andrej Varchola Ivan Viola Nicholas Waldin Manuela Waldner

ΔI  ΔO

Adjust, Just Adjust

slide-24
SLIDE 24
slide-25
SLIDE 25

References

[1] https://tctechcrunch2011.files.wordpress.com/2017/10/efp_170925_toyota‐autonomous‐ car_0085‐edit‐2.jpg?w=738 [2] http://vehiclepassion.com/wp‐content/uploads/2017/02/ehang‐184‐aav‐flying‐taxi‐in‐UAE.jpg [3] https://medium.com/@xenonstack/log‐analytics‐with‐deep‐learning‐and‐machine‐learning‐ 20a1891ff70e [4] Waldin, N., Le Muzic, M., Waldner, M., Gröller, E., Goodsell, D., Ludovic, A., Viola, I.: Chameleon ‐ Dynamic Color Mapping for Multi‐Scale Structural Biology Models. Proceedings of Eurographics Workshop on Visual Computing for Biology and Medicine (EG VCBM), Sep 7‐9, 2016, pp 11–20. (Honorable Mention Award) [5] Mindek, P., Mistelbauer, G., Gröller, E., Bruckner, S.: Data‐Sensitive Visual Navigation. Computers & Graphics 67: 77‐85 (2017) doi: 10.1016/j.cag.2017.05.012. (Special Section on SCCG 2017, Best Paper Award) [6] Varchola, A.: Live Fetoscopic Visualization of 4D Ultrasound Data. PhD Thesis, TU Wien (2012). [7] GE Healthcare. Voluson E8 Expert, available from http://www3.gehealthcare.com/en/Products/Categories/Ultrasound/Voluson/. Accessed: 2018‐ 01‐15.

Eduard Gröller 25

slide-26
SLIDE 26

Abstract

Adjust, Just Adjust Developing visualizations for broad audiences requires glanceable graphics and graspable interactions. This talk will concentrate on interaction facilitation through automatic adjustments. The first example illustrates an automatic color scale adjustment in a bio‐molecular setting to accommodate contradicting and

  • verlapping color schemes across scales. The second example discusses output‐

sensitive interaction to make changes in the input proportional to changes in the

  • utput, or to visually indicate the sensitivity of input changes with respect to
  • utput changes. The third example deals with visualization of 4D ultrasound data,

which is targeted to a broad audience in prenatal imaging and diagnosis. Lessons learned during this project are presented. The talk makes a case for automatically reducing interaction complexity in visualizations for broad audiences.

Eduard Gröller 26