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SP2.3: UI and VR Based Visualization

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)

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SP2.3: UI and VR Based Visualization

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  1. 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 7th, 2006

  2. 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)

  3. SP2.3 ongoing activities • Multi-spectral visualization SP 1.6 • Particle visualization SP 1.6 • Confocal Cell Imaging • Volume measuring SP 2.1 • Medical Imaging SP 1.4 • Virtual Reality on the GRID SP 3.1 • Distributed Scene Graphs SP 3.1

  4. SP 2.3 status • 25 international publications • 2 spin-offs • Foldyne (TU Delft) • Personal Space Technologies (CWI) • Projected output • 4 PhD thesis • At least 2 packages in PoC

  5. Collaboration SP 1.6 DUTELLA • Prof Ron Heeren (ALMOF) • Topic: Mass Spectrometry for molecular imaging • Motivation: need for better MS analysis tools • Visualization Topics: • Multi-spectral data visualization • In-silico mass spectrometry • Envisioned output: • GRID enabled toolbox for MS analysis • Applications according to VL-e methodology

  6. Problem: aligning multi-spectral data cubes • Multi-spectral data cube: 256x256x65k • Multiple data cubes • ±100 cubes in mosaic • Current procedure: manual alignment on pixel values

  7. Our novel approach • Idea: Align spectral features in adjacent samples • Approach: • Compute spectral features using PCA • For each feature, find a most optimal spatial alignment of the feature • The overall spatial alignment is optimal for all features

  8. MS beelden dijbeen muis

  9. First Spectral Feature = Principal Component1

  10. Second Spectral Feature Principal Component2

  11. use the combination of 2 local minima Minimization map of 2nd feature Minima landscape Minimization map of 1st feature

  12. 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

  13. 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 causes • Intra ion-cluster interactions?

  14. Our novel approach • Idea: simplify images with • Statistical parameterized icons • Semantic camera control • Approach: • Parameterized “comet-icons” • Camera motion relative to comet dynamics

  15. Example: icons • Ions groups • Statistical ion properties of group • Ion density dynamics

  16. Example: camera control • Trapping motion • Relative cyclotron frequency • Tracks of Frenet frames

  17. 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

  18. 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!

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