AMCS / CS 247 – Scientific VisualizationLecture 22: Vector Field / Flow Visualization, Pt. 6

Markus Hadwiger, KAUST

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Reading Assignment #12 (until Dec. 3)

Read (required):• Data Visualization book, Chapters 6.7, 6.8

• J. van Wijk: Image-Based Flow Visualization, ACM SIGGRAPH 2002http://www.win.tue.nl/~vanwijk/ibfv/ibfv.pdf

Read (optional):

• J. van Wijk: Image-Based Flow Visualization for Curved Surfaces,IEEE Visualization 2003http://www.win.tue.nl/~vanwijk/ibfv/ibfvs.pdf

• R. Laramee, B. Jobard, H. Hauser: Image Space Based Visualization of Unsteady Flow on Surfaces, IEEE Visualization 2003http://www.computer.org/portal/web/csdl/doi/10.1109/VISUAL.2003.125036

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Quiz #3: Dec. 3

Organization• First 30 min of lecture

• No material (book, notes, ...) allowed

Content of questions• Lectures (both actual lectures and slides)

• Reading assignments (except optional ones)

• Programming assignments (algorithms, methods)

• Solve short practical examples

Texture Advection

KAUST King Abdullah University of Science and Technology 4

Texture Advection

• Advect texture / images along flow

• Usually noise textures and dye

• Regularly inject new noise

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Also for unsteady flow, i.e.,time-dependent vector fields!

Lagrangian vs. Eulerian

• Lagrangian: move along with the particle

• Eulerian: consider fixed point in space, look at particles moving through

• Example for pixels: rotate image (a),Lagrangian: move pixels forward (b),Eulerian: fetch pixels from backward direction (c)

Markus Hadwiger, KAUST 6

Lagrangian-Eulerian Advection (LEA)

• Influence ofthe blend factorbetween freshnoise and theadvected noise

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Image Based Flow Visualization

J. van Wijk: “Image Based Flow Visualization” in Proceedings of ACM SIGGRAPH 2002

Image Based Flow Visualization

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Image Based Flow Visualization

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Algorithm

1. Calculate distorted mesh R (if flow field has changed)

2. Render R, texture mapped with previous image

3. Blend with noise image, using factor α (weight previous with 1 – α)

4. Draw (inject) dye if desired

Image Based Flow Visualization

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Blending of (low-pass filtered) noise images

Results in convolution with exponential kernel:

Exponential decay of image G

Image Based Flow Visualization

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Noise image generation

• Use interpolated image of random values: s is scale parameter

• h() is triangular pulse

• Random values on grid to interpolate: rate of change vg,random phase ϕ, periodic function w() (square, saw)

Image Based Flow Visualization

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Spatial noise frequency(scale parameter s)determines visual style

Like LIC

Like spot noise

Blurred

Image Based Flow Visualization

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Periodic function w()

Cosine

Square

Exponential decay

Saw tooth

Image Based Flow Visualization

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IBFV on Surfaces

J. van Wijk: “Image Based Flow Visualization for Curved Surfaces” in Proceedings of IEEE Visualization 2003

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IBFV on Surfaces (IBFVS)

IBFVS pipeline

• Advect noise patterns in directionof vector field

• Done using 3D vector fieldprojected to 2D image space→ flow visualization oncurved surfaces

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Image Space Flow Vis on Surfaces

R. Laramee, B. Jobard, H. Hauser: “Image Space Based Visualization of Unsteady Flow on Surfaces” in Proceedings of IEEE Visualization 2003

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Image Space Flow Vis on Surfaces

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Image Space Flow Vis on Surfaces

Meshes can be time-dependent

Example: intake valve and piston cylinder

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Example: Visualize Curvature Directions

Vector field is field of principal curvature directions

Thank you.

Thanks for material• Ronny Peikert

• Helwig Hauser

• Meister Eduard Groeller

• Jens Krüger