All water and air of the world 1
First prize category ‘Concepts’, 2003 Visions of c Dr Adam Nieman. Science Awards. (http://www.adamnieman.co.uk/vos/)
Visualization 2
What is visualization ? • visualize: ‘to form a mental image or vision of . . . ’ • visualize: ‘to imagine or remember as if actually seeing’ Visualization is a cognitive activity.
Birth of the field 3
ACM-SIGGRAPH rapport Visualization in Scientific Computing 1987: Visualization is a method of computing. It transforms the symbolic into the geometric, enabling researchers to observe their simulations and computations. Visualization offers a method for seeing the unseen. It enriches the process of scientific discovery and fosters profound and unexpected insights. In many fields it is revolutionizing the way scientists do science.
Motto 4
The purpose of visualization is insight, not pictures. Freely rendered from Richard Hamming
Chernoff faces for visualizing multivariate data.
Why Visualization ? 5
• Exploits dominance of human visual system, human pattern recognition capability • Essential for interpretation of data and hypothesis building in many scientific problems • Prominent role for interactive exploration
Classification 6
• Scientific visualization: forming visual representations of physical data (strong association to computational science) Examples: fluid flow, volume visualization, spatial data (GIS, remote sensing) • Information visualization (InfoVis): forming visual representations of abstract data Examples: network visualization, database visualization, program visualization, document visualization.
Classification (continued) 7
• Software-visualisation: Using visual representations to get insight in (parallel) computer programs, large modular software systems or the design process itself. • Mathematical visualization: Using computer-generated visualizations to point the way to rigorous mathematical proofs. • Visualisation in Virtual Environments: Immerse the user in a virtual (computer generated) environment, which evokes a strong psychophysical experience. Applications: training, simulation, architecture, amusement.
Application areas 8
Visualization always takes place within the context of some application area . . . • Physics, Chemistry, Biology, Astronomy • Medical applications (CT, MRI, . . . , brain activation studies) • Environmental science (weather/climate prediction) • Finance, marketing, internet, publishing . . . • ...
Diverse fields 9
application area
data analysis graphics
perception cognition
Problems 10
• Growth of data size: many techniques not scalable • Interaction with data is not flexible enough • Lack of visual metaphors for abstract data • Fidelity of graphical representations is limited • Human factor underestimated (perception, cognition) • Group interaction underdeveloped • Lack of standards and inter-operability • Validation methodology lacking
Graphical techniques 11
1950 first computer-controlled display point-plotting techniques 1965 interactive computer graphics: 2D graphics, line-drawing techniques, display files, graphical input techniques, 2D transformations 1970 3D graphics: object definition (curves and surfaces), scan-conversion of polygons, 3D transformations, perspective, illumination models 1985 scientific visualization as separate field
Visualization pipeline 12
Data generation
Data preparation
Data space
Mapping
Rendering
Graphic space Image space
Visualization of mathematical surfaces
Minkowski sum of two orthogonal circles (quartic surface (x2 − y 2 + z 2 + rz2 − ry2)2 = 4x2(rz2 − y 2)), ray tracing with texture mapping Roerdink & Blaauwgeers, Visualization of Minkowski operations by computer graphics techniques, Proc. ISMM’94
13
Document visualization 14
Self-Organizing Map of 32617 articles of newsgroup sci.lang (grey value ∼ document density). WEBSOM - websom.hut.fi/websom/
Volume visualization 15
• surface rendering reduce volume to isosurfaces S(c) : f (x, y, z) = c of a density function f (x, y, z) representing the boundary between materials. • direct volume rendering map volume data directly on screen (no graphical primitives) with semi-transparent effects forward projection
FTB
BTF
LSD molecule 16
LSD molecule docked inside human serotonin 5-HT2A receptor http://www.heffter.org/
Frog data: segmentation 17
• Data: physically slice the frog and photograph the slices. • 136 slices of resolution 470 × 500. • Each pixel labelled with tissue number from 1-15. Whole Frog Project, Computing Sciences, Lawrence Berkeley National Laboratory (www-itg.lbl.gov/Frog)
Frog data: Surface rendering 18
Frog data: Organs 19
Combined surface rendering-ray casting
skin: surface rendering
bones: ray casting
20
Software visualisation 21
The SequoiaView application gives insight into the contents of your hards disk within seconds.
Reality Center, RuG 22
Left: Reality Cube (‘Cave’); right: Reality theater
Multiple Views Visualization in VE 23
Multimodal visualization of functional brain data superimposed on anatomical data in a CAVE (Reality Center, University of Groningen).
Internationale Ontwikkelingen Visualization Research Challenges, U.S. National Institutes of Health (NIH) & National Science Foundation (NSF). • Veel van de huidige visualisatietechnieken worden verkeerd of in het geheel niet gebruikt. • Geen volledige automatisering: houd de gebruiker ‘in the loop’. • Belangrijk: perceptie/cognitie, exploratie/interactie en specificatie/visualisatie. • Visualisatie kan helpen bij vorming, evaluatie en exploratie van hypothesen.
24
Het visualisatie-ontdekkingsproces 25
Data
Visualisatie
Beeld
Specificatie
Data
Perceptie & Cognitie
Exploratie
Visualisatie
Gebruiker
Van Wijk 2005: “The value of visualisation”.
Kennis
Data explosie 26
• De grote verandering sinds het vorige rapport uit 1987 is de information big bang, die voornamelijk in digitale vorm wordt geproduceerd. • Het begrijpen en gebruikmaken van deze stortvloed aan informatie wordt gezien als de belangrijkste uitdaging voor de 21e eeuw.
Uitdagingen 27
• Gezondheidszorg. Genetische screening, bioinformaticaonderzoek, navigatie van medische data (b.v. beeldgestuurde chirurgie, neurochirurgie), en Personalized Medicine . • Wetenschap. Steeds grotere datastromen vereisen gedistribueerde visualisatiemethoden en -gereedschappen. • Engineering. Visualisatie dient methoden te ontwikkelen voor rapid prototyping. • Massamarkten. Een soort Visual Google is nodig die niet-experts helpt bij het oplossen van problemen op een visuele manier.
28
• Veiligheid. In de V.S. is hiertoe het National Visualisation and Analytics Center opgericht: nvac. pnl.gov. • Economie. Visualisatiesystemen voor gebruik in ¨ instellingen. bedrijven en financiele • Onderwijs. Grotere rol, zowel voor docent als student/leerling.
Nationale Ontwikkelingen 29
NWO-stimuleringsprogramma’s voor de informatica (I-Science). • VIEW (Visual Interactive Effective Worlds): Interactieve data visualisatie en Interactieve virtuele werelden. • STARE (STAR E-Science). Onderzoek op het grensvlak tussen informatica en astronomie. • Visualisatie in Nationale OnderzoeksAGenda ICT 2005-2010: De data-explosie; De digitale beleving; Het virtuele laboratorium: ict.stw.nl/noagict.
Nationale Ontwikkelingen 30
• Visualisatie van netwerken: NWO programma Biomoleculaire Informatica en het BioRange programma: www.nbic.nl. • Nederlandse Neuroinformatica Initiatief: www.neuroinformatics.nl.
Lokale Ontwikkelingen 31
• Groningen Visualisatie Centrum • Centrum voor High Performance Computing & Visualization
Onderzoeksgroep Wetenschappelijke Visualisatie en Computergrafiek
32
• Volume visualisatie • Multidimensionale data-analyse • Software visualisatie • Non-photorealistic rendering & Innovative Interfaces • Gebruik van graphics hardware • Toepassingen: Medische visualisatie, Neuroinformatica, Bioinformatica, Astronomie. Web: www.rug.nl/informatica/onderzoek/ programmas/svcg
Onderwijs 33
• Bachelor: Computer graphics, Innovative Interfaces. • Master: Image Processing, Scientific visualization, Advanced Computer Graphics, Pattern Recognition, Computer Vision.
