review of flow vis for lower dimensional datachengu/teaching/fall2012/lecs/lec13_14.pdf ·...
TRANSCRIPT
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Review of Flow Vis for Lower
Dimensional Data• Direct: overview of vector field, minimal computation, e.g. glyphs (arrows),
color mapping
• Texture-based: covers domain with a convolved texture, e.g., Spot Noise,
LIC, ISA, IBFV(S)
• Geometric: a discrete object(s) whose geometry reflects flow
characteristics, e.g. streamlines
• Feature-based: both automatic and interactive feature-based techniques,
e.g. flow topology
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Vector Field Visualization
in 3D
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Review of Data Structure
Regular (uniform), rectilinear, and structured grids
Alternative:tetrahedral volume elements:unstructured
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Direct Method (Arrow Plot)
Source:
http://docs.enthought.com/mayavi/mayavi/mlab.html
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Texture-Based MethodVolume LIC
• Victoria Interrante and Chester Grosch (IEEE Visualization 97).
• A straightforward extension of LIC to 3D flow fields.
• Low-pass filters volumetric noise along 3D streamlines.
• Uses volume rendering to display resulting 3D LIC textures.
• Very time-consuming to generate 3D LIC textures.
• Texture values offer no useful guidance for transfer function design due to lack
of intrinsic physical info that can be exploited to distinguish components.
� Very challenging to clearly show flow directions and interior structures through a
dense texture volume.
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Sparse noise + Hybrid Hanning-Ramp kernel (Zhanping Liu and et al., Journal of Image and Graphics 2001)
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Geometric-Based Methods
Streamlines:
Theory s(t) = s0 + ∫0≤u≤t v(s(u)) du
Practice: Numerical integration such as Euler, RK2, RK4, etc.
Important: interpolation scheme, seeding!!
Chen et al. Vis 2007
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Geometric-Based Methodsstreamribbons, streamtubes, stream surfaces, flow volumes
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streamribbon:a ribbon (surface of fixed width) always tangent to the vector field shows rotational (or twist) properties of the 3D flow
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Streamribbon generation:
• Start with a 3D point xi=0 and a 2nd one yi=0 in a particular dist. d,
i.e. |xi-yi|² =d²
• Loop:
• Integrate from xi to yield xi+1
• Do an integration step from yi to yield z
renormalize the distance between xi & z to d, i.e. yi+1 =xi +
d·(z-xi)/|z-xi|
• End streamribbon integration if necessary
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• The computation of stream surfaces is
similar to streamribbon.
• However, now the seeding points are
typically more than two.
• Also, during the integration, we may need
to adaptively add or remove seeds (i.e.
handling divergence, convergence, and
shear).
• Triangulating the stream surface between
neighboring streamlines is easy to achieve.
• What is the challenge?
What about Stream Surfaces?
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Where to put seeds to start the integration?
Seeding along a straight-line
Allow user exploration
Source: D.Weiskopf et al. 2007
Seeding along the direction that is
perpendicular to the flow leads to
stream surface with large coverage
Source: Edmunds et al. EuroVis2012
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How about automatic stream surface placement?
Where to start? How to proceed?
Source: Edmunds et al. TPCG 2012
Render
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Rendering of stream surfaces
• Stream arrows (Löffelmann et al. 1997)
• Texture advection on stream
surfaces (Laramee et al. 2006)
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Rendering of stream surfaces
Illustrative visualization
• Using transparency and
surface features such as
silhouette and feature
curves.
Source: Hummel et al. 2010
Abraham/Shaw’s illustration, 1984 Source: Born et al. Vis2010
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16
http://cs.swan.ac.uk/~csbob/te
aching/csM07-vis/
Geometric FlowVis in 3Dflow volume: a volume whose
surface is everywhere tangent
to the flow
streamtube: shows convergence
and divergence of flow
(similar to streamribbon)
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Relation to Seed ObjectsObject Seed Object
Dimensionality Dimensionality
_________________________________________________________________________
Streamline,… 1D 0D (point)
Streamribbon 2.5D 1D (line segment)
Streamtube 2.5D 1D (circle)
Stream surface 2.5D 1D (curve)
Flow volume 3D 2D (patch)
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Feature-Based MethodsTopology of 3D Steady Flows
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• Fixed points
• Can be characterized using 3D Poincaré
index
• Both line and surface separatrices exist
3D Flow Topology
saddle-node
saddle-spiral
spiral-sink
node-source
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• Similar principle as in 2D
– Isolate closed cell chain in which streamline
integration appears captured
– Start stream surface integration along boundary
of cell-wise region
– Use flow continuity to exclude reentry cases
3D Cycles
Challenging to strange attractor
Source:
http://www.stsci.edu/~lbradley/seminar/attractors.html
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3D Cycles
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3D Topology Extraction
• Cell-wise fixed point extraction:
– Compute root of linear / trilinear expression
– Compute Jacobian at found position
– If type is saddle compute eigenvectors
• Extract closed streamlines
• Integrate line-type separatrices
• Integrate surface separatrices as stream
surfaces
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Saddle Connectors
Topological representations of the Benzene data set.
(left) The topological skeleton looks visually cluttered due to the shown
separation surfaces.
(right) Visualization of the topological skeleton using connectors.
Source: Weinkauf et al. VisSym 2004
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Saddle Connectors• Multiple separating surfaces may lead to occlusion
problems
• Idea: reduce visual clutter by replacing stream
surfaces with streamlines of interest
• Saddle Connector: – Separating surfaces intersection
integrated from two saddle points
of opposite indices (inflow vs.
outflow surface)
– Intersection is a streamline
Source: Theisel et al. Vis 03
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Saddle Connectors
Flow behind a circular cylinder:
13 fixed points and 9 saddle connectors have been detected and visualized. Additional LIC
planes have been placed to show the correspondence between the skeleton and the flow.
Source: Theisel et al. 2003
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Time-Dependent (Unsteady) Flow
Visualization
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What is Different?
Steady (time-independent) flows:
• flow itself constant over time
• v(x), e.g., laminar flows
• simpler case for visualization
Time-dependent (unsteady) flows:
• flow itself changes over time
• v(x,t), e.g., turbulent flow
• more complex case
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Mathematical Framework
• An unsteady vector field
– is a continuous vector-valued function �� �, � on a
manifold X
– can be expressed as a system of ODE ��
��� �� �, �
– is a map ϕ : R× X → X
– with initial condition � � � �, its solution is
called an integral curve, trajectory, or orbit.
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Stream, Path, and Streaklines
Terminology:
• Streamline: a curve that is everywhere tangent to the steady flow (release 1 massless particle)
s(t) = s0 + ∫0≤u≤t v(s(u)) du
• Pathline: a curve that is everywhere tangent to an unsteady flow field (release 1 massless particle)
s(t) = s0 + ∫0≤u≤t v(s(u), u) du
• Streakline: a curve traced by the continues release of particles in unsteady flow from the same position in space (release infinitely many massless particles)
Each is equivalent in steady-state flow
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Difference Between Streamlines and
Pathlines
A moving center over time: (left) streamlines; (right) path lines
Source: Weinkauf et al. Vis2010
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Streaklines• Not tangent curves to
the vector fields
• Union of the current
positions of particles
released at the same
point in space
Source: wikipedia
Weinkauf et al. 2010
timelines• Union of the current
positions of particles
released at the same
time in space
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Characteristics of Integral Lines
Advantages:
• Implementation: various easy-to-implement streamline tracing algorithms (integration)
• Intuitive: interpretation is not difficult
• Applicability: generally applicable to all vector fields, also in three-dimensions
Disadvantages:
• Perception: too many lines can lead to clutter and visual complexity
• Perception: depth is difficult to perceive, no well-defined normal vector
• Seeding: optimal placement is very challenging (unsolved problem)
Source: Marchesin et al. Vis 2010
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Illustrative path surfaces: texture + transparency [Hemmel et al. 2010]
Path surface:
The extension of the path lines into unsteady 3 dimensional flows
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Path surface:
Its computation can use time line advection
Source: Garth Vis09 Tutorial
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Streak surfaces are an extension of streak
lines (next higher dimension)
Challenges:
• Computational cost: surface advection is
very expensive
• Surface completely dynamic: entire
surface (all vertices) advect at each
time-step
• Mesh quality and maintaining an
adequate sampling of the field.
• Divergence
• Convergence
• Shear
• Large size of time-dependent (unsteady)
vector field data, out-of-core techniques
Streak Surfaces: Challenges
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A Streak Surface Computation Pipeline
Divergence: Quad Splitting
Shear
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Time Lines on Streak Surfaces
Source: Garth et al. Vis 2008
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Texture-Based Methods
Unsteady flow LIC (UFLIC): forward scattering + collecting
IBFV: texture advection in forward direction + hardware acceleration
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Topology-based Method
• Parameter dependent topology:• “Fixed points” (no more fixed) move, appear, vanish,
transform
• Topological graph connectivity changes
• Structural stability: topology is stable w.r.t. small but arbitrary changes of parameter(s) if and only if• 1) Number of fixed points and closed orbits is finite and all
are hyperbolic
• 2) No saddle-saddle connection
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Bifurcations
• Transition from one stable structure to
another through unstable state
• Bifurcation value: parameter value inducing
the transition
• Local vs. global bifurcations
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Local Bifurcations
• Transformation affects local region
• Fold bifurcation: saddle + sink/source
source sink unstable no fixed point
1D equivalent:
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Local Bifurcations
• Transformation affects local region
• Hopf bifurcation: sink/source + closed orbit
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Global Bifurcations
• Affects overall topological connectivity
• Basin bifurcation
Saddle-saddle connection
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Global Bifurcations
• Modifies overall topological connectivity
• Homoclinic bifurcation
Saddle-saddle connection
repelling cycle (source)
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• Modifies overall topological connectivity
• Periodic blue sky
Global Bifurcations
Homoclinic connection
center
sink source
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• Time-wise interpolation
• Cell-wise tracking over 2+1D grid
• Detect local bifurcations
• Track associated separatrices (surfaces)
2+1D Topology
time
prism cell
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2+1D Topology
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2+1D Topology
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• Feature Flow Field
– Track path of critical points by streamline integration in vector field defined over (n+1)D space-time domain
– The value of (e.g. ) is constant along streamlines of
2+1D Topology
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2+1D Topology
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2+1D Topology
• Combined with stream surface integration in
space-time domain: moving separatrices
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Additional Readings
• Matthew Edumunds, Robert S. Laramee, Guoning Chen,
Nelson Max, Eugene Zhang, and Colin Ware, Surface Based
Flow Visualization, Computers & Graphics, forthcoming.
• Tony McLoughlin, Robert S. Laramee, Ronald Peikert, Frits H.
Post, and Min Chen, Over Two Decades of Integration-Based,
Geometric Flow Visualization in Computer Graphics Forum
(CGF) , Vol. 29, No. 6, September 2010, pages 1807-1829.
• Tino Weinkauf and Holger Theisel. Streak Lines as Tangent
Curves of a Derived Vector Field. IEEE Visualization 2010.
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Acknowledgment
Thanks for the materials
• Prof. Robert S. Laramee, Swansea University,
UK
• Dr. Christoph Garth, University of
Kaiserslautern, Germany
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Final Project Topic Overview
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Advanced texture-based flow
visualization
• IBFVS – Image-based Flow Visualization on
Curved Surfaces
1) Initialize image
2) Calculate the texture coordinates for all
vertices
3) Texture mapping using previous saved image
4) Blend with fresh white noise
5) Save current image for next frame texture
mapping
6) Blend with shaded surface
7) Other post image process
8) If current state is changed, say camera
position is changed, the projected vector field
is changed subsequently, go back to 2),
otherwise, go back to 3)
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Advanced texture-based flow
visualization
• ISA – Image-Space
Texture Advection
1) Project geometric mesh and its associated vector field onto the image space to get a
velocity image
2) Perform edge detection to avoid unnatural continuities
3) Compute the advection of the mesh in image space using the similar method as IBFV
4) Map the previous image to the distorted mesh
5) Blend fresh noise to keep contrast
6) Blend extra gray value to highlight the discontinuities at the edges detected in 2)
7) Overlap with the attributions of the surfaces
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Advanced texture-based flow
visualizationVolume LIC
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Vector field topology
• ECG versus MCG
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Vector field topology
• 3D topology
Saddle connectors:
H. Theisel, T. Weinkauf, H.-C. Hege, H.-P. Seidel. Saddle connectors-an approach to
visualizing the topological skeleton of complex 3D vector fields. IEEE Visualization
2003, pp. 225-232
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Vector field topology
Out-of-core vector field analysis
new research, no much work on this
topic yet
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Time-varying vector field analysis
• Stable feature flow for feature tracking
T. Weinkauf, H. Theisel, A. Van Gelder, and A. Pang. Stable Feature Flow
Fields. IEEE Transactions on Visualization and Computer Graphics 17(6),
June 2011
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Time-varying vector field analysis• FTLE analysis
– Lagrangian coherent structure (LCS)
Ridges in Lyapunov exponent
Transient view
Confluences
LCS = interface
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Time-varying vector field analysis
• FTLE analysis
– FTLE is the “growth of
perturbation after advection
time T”.
For flow:
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3D (time-varying) vector field
visualization
• Geometric-based method
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Scalar field analysis
• Reeb graph computation
V. Pascucci, G. Scorzelli, P.-T.
Bremer, and A. Mascarenhas,
“Robust On-line Computation of
Reeb Graphs: Simplicity and
Speed”, ACM Transactions on
graphics, pp. 58.1-58.9,
Proceedings of SIGGRAPH 2007.
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Scalar field analysis
• Morse-Smale complex computation
A. Gyulassy, P.-Ti. Bremer, B. Hamann, and
V.Pascucci. “A Practical Approach to Morse-
Smale Complex Computation: Scalability and
Generality”. IEEE Transactions on Visualization
and Computer Graphics, 14(6), 2008, 474-484.
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Asymmetric tensor field visualization
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Asymmetric tensor field visualization
Guoning Chen, Darrel Palke, Zhongzang Lin, Harry Yeh, Paul Vincent, Robert S. Laramee and
Eugene Zhang. "Asymmetric Tensor Field Visualization for Surfaces", IEEE TVCG (Proceeding
of IEEE Visualization 2011), Vol.17, No. 12, pp 1979-1988, 2011.
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Diffusion tensor imaging
Gordon Kindlmann and Carl-Fredrik
Westin. Diffusion tensor visualization with
glyph packing, IEEE Visualization 2006.
X. Tricoche, G. Kindlmann, and C.-F. Westin,
Invariant crease line s for topological and
structural analysis of tensor fields, IEEE
Visualization 2008
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GPU-based real-time volume
rendering
• GPU-based ray-casting + iso-surface rendering
Open source for your reference: Exposure render
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Illustrative visualization
Illustrative visualization of vector fieldsIllustrative visualization of scalar fields
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Information visualization• Large scale dynamic graph visualization
Michael Burch, Corinna Vehlow, Fabian Beck,
Stephan Diehl, and Daniel Weiskopf. Parallel Edge
Splatting for Scalable Dynamic Graph Visualization.
IEEE Information Visualization 2011.
Open source: https://gephi.org/
http://www.caida.org/tools/visualization/walrus/
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Information visualization
• Higher dimensional data visualization
What is a person’s life span given gender, annual income,
living area, marriage status, medical care, smoking or not, …
What will determine/affect the temperature?
Location (x, y, z), time in a day, humidity, pressure, cloudy or
not, windy or not, human activities, …
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Information visualization
• Higher dimensional data visualization
Parallel coordinates
Scatter plot matrix
What else?
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Others?
name a good project with some merit of visualization then we can discuss.
3B: Be creative! Be aggressive! Be ambitious!