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Feature-Based Iso-Surface Visualization for Terascale Datasets

Efficient visualization and exploration of large datasets to detect and rank important features, compress data, and allow user interaction for optimal information access. Research includes wavelet analysis, feature detection, and coding methods.

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Feature-Based Iso-Surface Visualization for Terascale Datasets

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  1. Efficient Visualization and Interrogation of Terascale Datasets Gheorghe Craciun, Ming JiangRaghu Machiraju The Ohio State University Roy Rong, Li Hua, Sridhar Dusi, Jaya Nair, Sajjit ThampyJames Fowler David Thompson Bharat Soni Engineering Research Center, Mississippi State University Hari IyerWilliam Schroeder Rensselaer Polytechnique Institute

  2. Visualization CT, MRI, Laser, Ultrasound Numerical Simulations

  3. Iso-Surfaces Find Implicit Surface s = f(x,y,z)

  4. More Iso-surfaces

  5. Particle Tracing

  6. Simulations, scanners State-Of-Affairs ? • Concurrent • Presentation • Retrospective • Analysis • Representation

  7. Another Real Problem ! • Data is too complex; What is a good iso-value ?

  8. Bottom Line ... • Exploration and visualization too slow ! • Large parameter space • Too much information • Cumbersome display and interaction devices understanding amount

  9. Goal:Maximize information (features) access while minimizing data rate One Method Seek features Rank them Access data in view frustum Give cues for interesting visual filters Visualization Solution ?

  10. Explore large (terascale) datasets: Detect features that may be of importance Segment features into regions-of-interest (ROI) Rank ROIs, rank information within ROIs Compress data and grid for ROIs Progressive visualization of ROIs Allow user to change order of progression Provide exploration tools to determine features Solution …

  11. Why Feature-Based ? Magnitude Based Missing ! Reconstructed 1% Rate Feature Based

  12. Is This Data Mining ? • Yes ! • It is structure-based • Basic premise --- Features and their shapes are correlated manifestations of simulation parameters • Once shapes are determined do all mining on shape descriptors !

  13. ROIs displayed with successively increasing resolution (fidelity) Progressive Visualization

  14. EVITA System Operation Background is ROI 0

  15. EVITA System

  16. Final Goal !!

  17. EVITA System Demo

  18. Interrogative Techniques Scatter Plots Characteristic Curves

  19. EVITA System

  20. Preprocessor

  21. Server

  22. Client

  23. Feature Detection: Detection of significant features in wavelet domain Ranking of features for visualization Tracking features through space & time Really Structure or Region Mining !! Research Issues

  24. Coding & Compression: Efficient compression of vector fields & grids Embedded coding of significant features Interactive ROI trans-coding Research Issues

  25. Visualization: Interrogative techniques Interactors for 4D space-time navigation Research Issues

  26. Feature Detection: Detection of significant features in wavelet domain Design feature preserving transforms Basic Premise: Transformations should not destroy features and their shapes Research: Wavelet Transform

  27. Wavelet Analysis L w L=(1/2,1/2) K=(1/2,-1/2) L N sample points O(N) Algorithm N coefficients L

  28. Discontinuity As A Shape !

  29. Popular Filter 1

  30. Popular Filter 2

  31. Complicated Discontinuity !

  32. Stream Function

  33. Popular Filter 2

  34. Application of linear filter = evolving solution of PDE S is function and Dx, Dt are space and scale (time) resolutions Going from fine to coarse scales PDE Framework- Model Equations a, b, c, d: moments of filter coefficients

  35. Linear (Heat Equation) : no phase shift Shape change but not location Analysis of Wavelet Schemes • Haar (Wave Equation): • phase shift – shapes move • amplitude damping – shapes change

  36. Total Variation Diminishing S|sln+1-skn+1| < S |snl-skn| • Need more for shapes • Limiting growth of functions • Will not allow for new maximas or minmas …

  37. High res Low res A Filter Design Axioms for A • Partition of Unity • Symmetric Function • Accuracy of order p for smooth data • TVD • Stable Transform • Distance to Sinc Function • Implement as filter bank

  38. (1/4, 1/2, 1/4) is shortest filter that, for p=1, satisfies axioms – linear TVD Filter of length 5 that satisfies axioms for largest possible p(1/16, 1/4, 3/8, 1/4, 1/16) and p=4. Design can be achieved through optimization procedure Find something close to ideal filter (since filter) Examples

  39. TVD Scheme • Linear Symmetric TVNI: • no phase shift, amplitude decrease

  40. Feature Preservation Total Variation Diminishing (TVD)

  41. TVD

  42. 3D Results • Cubic wavelet Result (not TVD) • TVD wavelet Result • Original Image

  43. Objective: Identify different types of features for CFD solutions on structured grids Feature catalog Stationary and transient shocks Expansion regions Vortices Separation and attachment lines Regions of separated flow Research: Feature Detection

  44. Inexpensive method to find core ! Should be easily done at all scales Involve underlying physics Portela (1997): A vortex is comprised of a core region surrounded by swirling streamlines Design algorithm based on intuition Detect vortex core region Verify using swirling streamlines Feature matching and feature tracking are straightforward Basic Approach

  45. 3D Rankine Vortex

  46. Point-based approach using ideas from combinatorial topology Sperner’s Lemma: Every properly labeled subdivision of a simplex has an odd number of distinguished simplices Brouwer’s Fixed Point Theorem: Every continuous mapping has a fixed (critical) point - ( to stirring coffee in cups  ) Detection Algorithm

  47. At least one subtriangle in a Sperner labelingreceives all three labels: {A, B, C} Sperner’s Lemma

  48. Vector Field Labeling Labeling scheme 2D vector field

  49. 2D Algorithm • Simple and efficient! • Point-based approach: • Label neighbors • Combinatorial: • Locally check for complete triangles

  50. 2D Results Rankine Vortices Wake Simulation

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