sp2.3: ui and vr based visualization
DESCRIPTION
SP2.3: UI and VR Based Visualization. Partners: TU Delft, VU, CWI. Ongoing Activities and progress Collaboration Highlight with SP 1.6 DUTELLA. R. van Liere April 7 th , 2006. SP 2.3 people. 4 PhD students: Broersen, Burakiew, Kruszynski (CWI) - PowerPoint PPT PresentationTRANSCRIPT
Partners: TU Delft, VU, CWI
SP2.3: UI and VR Based Visualization
• Ongoing Activities and progress• Collaboration Highlight with SP 1.6 DUTELLA
R. van Liere April 7th, 2006
SP 2.3 people
4 PhD students: Broersen, Burakiew, Kruszynski (CWI)van der Schaaf (VU)
3 PD: Botha, Koutek (TUD)de Leeuw (CWI)
4 supervision: van Liere (CWI) Post, Jansen (TUD)Bal (VU)
SP2.3 ongoing activities
Multi-spectral visualization SP 1.6Particle visualization SP 1.6Confocal Cell ImagingVolume measuring SP 2.1Medical Imaging SP 1.4Virtual Reality on the GRID SP 3.1Distributed Scene Graphs SP 3.1
SP 2.3 status
25 international publications 2 spin-offs
Foldyne (TU Delft)Personal Space Technologies (CWI)
Projected output4 PhD thesisAt least 2 packages in PoC
Collaboration SP 1.6 DUTELLA
Prof Ron Heeren (ALMOF)
Topic: Mass Spectrometry for molecular imagingMotivation: need for better MS analysis toolsVisualization Topics:
Multi-spectral data visualizationIn-silico mass spectrometry
Envisioned output:GRID enabled toolbox for MS analysisApplications according to VL-e methodology
Problem: aligning multi-spectral data cubes
Multi-spectral data cube: 256x256x65kMultiple data cubes
±100 cubes in mosaicCurrent procedure: manual alignment on pixel values
Our novel approach
Idea: Align spectral features in adjacent samples
Approach:Compute spectral features using PCAFor each feature, find a most optimal spatial alignment of the featureThe overall spatial alignment is optimal for all features
MS beelden dijbeen muis
First Spectral Feature = Principal Component1
Second Spectral Feature Principal Component2
Minima landscape
Minimization map of 2nd feature
use the combination of 2 local minima
Minimization map of 1st feature
Impact ? Generic ? GRID?
Faster, unsupervised objective reproducible alignment combined with VL inspection tools for SP1.6
Method can also be applied to multi-spectral data cubes from other types of microscopes/telescopes.
Data-cube:256x256x65K. 100 cubes. Alignment:15min in Matlab. Combinations: (100 2) * 15
Problem: Meaningful ion dynamics
Ion clouds: ~50k ions x 1M steps Current visualizations are low level, eg.:
But how about: Intra ion-cluster interactions and their causesIntra ion-cluster interactions?
Our novel approach
Idea: simplify images withStatistical parameterized iconsSemantic camera control
Approach:Parameterized “comet-icons”Camera motion relative to comet dynamics
Example: icons
Ions groupsStatistical ion properties of groupIon density dynamics
Example: camera control
Trapping motionRelative cyclotron frequencyTracks of Frenet frames
Impact ? Generic ? GRID?
Improvement of mass accuracy understanding/control leads to enhanced protein ID in proteomics
Software framework is targeted towards particle visualization. Semantics of icons/cameras can be added/changed/enhanced
Near-future: optimization of simulation initial conditions
Final SP 2.3 comments
SP 2.3 is well on track
Projected output:GRID enabled toolbox SP2 layer Applications using toolbox SP1 layer
However: visualization PhDs are not mass spectrometry scientists!