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Chaotic CT reconstruction.
Alex Zamyatin, Bio-Imaging Research
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3D cryo-static micro CT of some snow and some of the individual snow flakes extracted
from that 3D image.
E. L. Ritman et al., Mayo Clinic College of Medicine
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TIFF
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Seismic reflection image of a vertical slice
through the upper 3 km of the Earth's crust.
Kidane Araya
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Electromagnetic wave in the Fujisawa-Koshiba photonic crystal with a waveguide bend.
Igor Tsukerman
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Yellow lily reconstructed from hyperspectral data.
Foster,
D. H., Nascimento, S. M. C. & Amano, K. (2004).
Information limits on neural identification of colored surfaces in
natural scenes. Visual Neuroscience 21, 331-336.
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BMP
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The depth of brain sulci in axial, coronal and sagittal
view
Chiu-Yen Kao, IMA
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High dynamic range volume rendering result for turbulent
mixing of air
and Sulfur Hexafluoride(SF6). Left: tone mapped image from
high dynamic range volume rendering; right: images at different
exposure
levels from the same rendering.
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Xiaoru Yuan, Minh X. Nguyen, Baoquan Chen and David H.
Porter, Dept. of Computer Science and Engineering
University of Minnesota,
High
Dynamic Range Volume Visualization."
In Proceedings of IEEE Visualization 2005, pages 327-334.
Minneapolis,
MN, USA. Oct 23 - 28, 2005.
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BMP
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(a) is at a time when the turbulence is in
the process of developing.
(b) is at
a time when the turbulence is fully developed.
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High dynamic range volume rendered Images depict
magnitude of vorticity from a high-resolution
simulation of homogenous decaying compressible uid
turbulence.
Xiaoru Yuan, Minh X. Nguyen, Baoquan Chen and David H.
Porter, Dept. of Computer Science and Engineering
University of Minnesota,
High
Dynamic Range Volume Visualization."
In Proceedings of IEEE Visualization 2005, pages 327-334.
Minneapolis,
MN, USA. Oct 23 - 28, 2005.
(a) JPG
(b) BMP
(b) JPG
(b) BMP
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Illustrative rendering of a simulated Fullerene
(C60) molecular model.
more images see:
http://www-users.cs.umn.edu/~xyuan/research/publication/isv.htm
Xiaoru Yuan and Baoquan Chen , Dept. of Computer Science and Engineering
University of Minnesota,
"Illustrating Surfaces in
Volume." In
Proceedings of Joint IEEE/EG Symposium on Visualization
(VisSym'04),
pages 9-16. Konstanz, Germany, May 19-21, 2004.
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Finger position risk landscapes for lifting and
touching a cylinder, respectively.
Erik J. Schlicht and Paul R. Schrater, Computational Perception and Action
Laboratory,
University of Minnesota
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Part 1 is the 3
dimensional view of
the convergents.
Part 2 is the projection of the convergents
onto the
complex plane revealing the structure of the orbits.
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scaled odd (blue/green/cyan) and even
(red/yellow/orange/magenta) convergents of a continued
fraction of
Ramanujan with complex coefficients.
Russell Luke, Department of Mathematical Sciences,
University of Delaware
Part
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Part
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Play avi file
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Reconstruction of the acoustic field in the
plane from boundary measurements in the far field.
Russell Luke, Department of Mathematical Sciences,
University of Delaware
AVI
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Figure 1: A comparison of classical transfer function based
classification
(left) and probabilistic classification (right) of brain data,
showing cerebro-spinal fluid, white matter, and gray
matter.
Figure 6: An illustration of Graph-based Data-space
Reparameterization
of
fuzzy classified data. Both datasets, the engine (top) and
Brainweb
Phantom (bottom), were reparameterized from a 10 dimensional
probability data-space to a 3D data-space. The slices on
the left
were colored by mapping the 3D reparameterized data directly to
the
RGB color space. The graphs on the right show how the
individual
classes' data values are arranged in the new 3D data-space.
Figures 1 and 6 combined, using only the
brain data from Figure 6
Top: A comparison of classical transfer function based
classification (left) and probabilistic classification (right)
of
brain data, showing cerebro-spinal fluid, white matter, and
gray
matter.
Bottom: An illustration of Graph-based Data-space
Reparameterization of fuzzy classified data. The Brainweb
Phantom
data was reparameterized from a 10 dimensional probability
data-space to a 3D data-space. The slice on the left was
colored
by mapping the 3D reparameterized data directly to the RGB
color
space. The graph on the right show how the individual
classes' data
values are arranged in the new 3D data-space.
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Joe Michael Kniss, School of Computing,
Scientific Computing and Imaging Institute, University of Utah
Figure 1: JPG
TIF
Figure 6: JPG
TIF
Combined Figures
1 and 6: JPG
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Tensorlines and superquadric glyphs
Gordon Kindlmann, Dr. David Weinstein, Scientific Computing and Imaging Institute,
University of Utah
Some tensor-line fiber tracts and superquadric tensor glyphs
used to depict some
of the white matter structure in a DT-MRI scan. The tensorlines
have been
highlighted for emphasis. Determining the extent to which the
the computed fiber
tracts actually correspond to white matter pathways is a
subject of ongoing work.
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Interactive Level-Set Segmentation and Volume Rendering of a
Brain Tumor from MRI Data
Aaron E. Lefohn, Joe M. Kniss, Charles D. Hansen, Ross T.
Whitaker, Scientific Computing and
Imaging Institute, University of Utah
A slice through a segmented brain tumor from an MRI volume. The
brown surface is the level-set segmented surface and the yellow
is
the intersection of this surface with the clipping plane. The
blue volume rendering gives the segmentation context within the
patient's head. The level-set surface is defined with an
interactive segmentation tool. The level-set and volume
rendering
computations are both interactively computed entirely on the
graphics processing unit (GPU).
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PNG
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Interactive Level-Set Segmentation and Volume Rendering of the
Cerebral Cortex from MRI Data
Aaron E. Lefohn, Joe M. Kniss, Charles D. Hansen, Ross T.
Whitaker, Scientific Computing and
Imaging Institute, University of Utah
A slice through a segmented cerebral cortex from an MRI volume.
The brown surface is the level-set segmented surface and the
yellow
is the intersection of this surface with the clipping plane.
The blue volume rendering gives the segmentation context within
the
patient's head. The level-set surface is defined with an
interactive segmentation tool. The level-set and volume
rendering
computations are both interactively computed entirely on the
graphics processing unit (GPU).
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Dragon distance field sampled with a particle system
Miriah Meyer, Pierre Georgel, Ross Whitaker,
Scientific Computing and Imaging Institute, University of
Utah
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Anisotropic smoothing
Tolga Tasdizen, Ross Whitaker, Scientific Computing
and Imaging Institute, University of
Utah
Mean curvature computed after anisotropic smoothing of surface.
Noise is smoothed and details are preserved.
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PNG
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Image Based Phenotyping using the Mouse Hox Genes as a
Prototype System
R. Whitaker, M.R. Capecchi, L. McAninch-Healy, Z.B. Warnock,
A.
Zharkikh, A.M. Boulet, Scientific Computing and Imaging
Institute, University of Utah
Segmentations of assorted mouse metacarpals and phlanges.
Segmentations were done using the Insight ToolKit (ITK)
(National
Library of Medicine). The segmentation algorithms within ITK
were engineering by the SCI Institute.
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Visualization of Spiny Dendrite Using Level-Set Surface Models
Vidya Elangovan, Ross Whitaker, Scientific Computing
and Imaging Institute, University of Utah
Microscopic electron tomography produces noisy 3D data, which
can be visualized using level-set surface model, which fits the
data
while preserving some level of continuity and smoothness.
Electron tomography data courtesy of Mark Ellisman, National
Center for Microscopy and Imaging Research.
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Fourier basis functions of Japan (lowest
25 "frequencies")
Naoki
Saito, Department of Mathematics,
University of California, Davis
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EPS
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Image visualizing vertical movement of data points during
constrained terrain regularization
Michael Hofer,
Guillermo Sapiro, Department of Electrical and
Computer
Engineering, University of Minnesota and
Johannes Wallner, Institute of Discrete Mathematics
and
Geometry, Vienna
University of Technology, Austria
Fair polyline networks for constrained smoothing of digital
terrain elevation data.
IMA Preprint 2058, University of
Minnesota, August
2005.
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Computer generated painterly image rendered from a 3D
laser-scanned
dataset of the Stone Arch Bridge, Minneapolis
Hui Xu, Nathan Gossett and Baoquan Chen, Department
of Computer Science
and Engineering, University of Minnesota
PointWorks: Abstraction and Rendering of Sparsely Scanned
Outdoor
Environments, In Proceedings of the 2004 Eurographics Symposium
on
Rendering (EGSR'04). Norrköping, Sweden, Jun 21-23, 2004
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a. Computer generated stippling rendering of the
Mount Rushmore
b. Demonstrates a novel viwing angle of the Mount
Rushmore from the captured data set.
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Capturing and Stylized Rendering of Mount Rushmore
Hui Xu and Baoquan Chen, Department of Computer
Science and Engineering,
University of Minnesota
Stylized Rendering of 3D Scanned Real World Environments, In
Proceedings
of the 3rd International Symposium on Non-Photorealistic
Animation and
Rendering (NPAR'04). Annecy, France, Jun 7-9, 2004
a. JPG
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Edges in Schlieren data of reacting turbulent
flow via mean-curvature dependent filtering
Walter
Richardson, Department of Mathematical Sciences,
The University of Texas at San Antonio
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Cross-sections of random samples from a family of
3D binary Markov random fields
Hstau Liao, The Graduate Center, City University of New York
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Beamformer image of tumors based on UWB microwave backscatter
from a
numerical breast phantom
Shakti Davis, Susan Hagness, and Barry Van Veen,
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Figure 1: Boy surface with triple point
The Boy surface is a model of the real projective plane. Each
immersion of the projective plane must have self-intersection and at least one
triple
point where three parts of the surface meet. The
self-intersection is emphasized by the thicker red line which
also highlights the
triple point. This model is colored by Gauss curvature.
Figure 2: Tessellation of the hyperbolic plane
Tessellation of the hyperbolic plane with a triangle (2,3,7).
The numbers determine how many (twice as much) triangles fit
around
each vertex.
Figure 3: Geodesic curves on a pretzel
Geodesic curves are the shortest and straightest lines on a
curved surfaces. Physically geodesics can be produced by
letting a
rubber band vary on a surface with both end points been fixed.
The pretzel world shows that there may be more than one
geodesic
connecting two locations, each of them is a local minimum for
the length functional.
From video "Geodesics and Waves"
http://www.zib.de/polthier/video/
Geodesics/index.html
Figure 4: The ideal Platonic solids of hyperbolic 3-space
The four ideal Platonic solids in hyperbolic space shown in an
arrangement over a hyperbolic plane. Each solid is enclosed in
an own
glass ball.
Figure 5: Klein Bottle with Moebius Band
The Klein bottle is a non-orientable surface found by Felix
Klein in 1882 while working on a topological classification of
surfaces.
There exists two Moebius bands on a Klein bottle. The second
band is symmetric to the shown Moebius band.
Article "Imaging maths - Inside the Klein bottle"
at http://plus.maths.org/
issue26/index.html
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Konrad Polthier, Zuse Institute Berlin (ZIB)
http://www.zib.de/polthier/images
Figure
1: JPG
Figure
2: JPG
Figure
3: JPG
Figure
4: JPG
Figure
5: JPG
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Figure 1: Height map for a textile texture from a flatbed scanner
Figure 2: Height + colour map for a textile texture from
a flatbed
scanner
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Andy Spence and Mike Chantler, Texture Lab,
Heriot-Watt
University, Edinburgh, UK
We present results from the application of our algorithm for
producing 3D
texture data from a small number of images acquired using an
ordinary flatbed
scanner. The corresponding height, bump and colour maps may be
utilised to
render mixed reality scenes with photorealistic textures.
For more textures, check http://www.macs.hw.ac.uk/texturelab/scan/texturescan.html
Figure 1 JPG
Figure 2 TIFF
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SURFACE INACCURACIES. Intersection of gray-matter (GM) and
white-matter (WM) CLASP surfaces: the GM surface is rendered in
red, the
corresponding WM surface in green. White circles indicate an
intersection of the GM and WM surfaces (cutaway view). In the
right
lower panel BRAINVISA surfaces-- WM (blue) and GM (red)--are
embedded in
an axial brain slice; visible flaws include a midline crossing
(A), an
inaccurate WM-GM boundary (B), failure to penetrate a sulcus
(C), and an
inaccurate GM-CSF boundary (D).
David Rottenberg, University of Minnesota
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Diffraction tomography sinograms for isotropic and anisotropic
cylinder/cube, and their difference
Matthew Lewis, Advanced Radiological Sciences,
UT Southwestern Medical Center at Dallas
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fMRI in a cat at 9.4 Tesla. The color is high resolution
(.15x.15x2 mm) BOLD activation patterns in response to
visual stimulation across the LAYERS in primary visual cortex,
overlayed on a high resolution T1 image.
Steen Moeller and Noam Harel, Center for Magnetic
Resonance
Research, University of Minnesota
Noam Harel, Joseph Lin, Steen Moeller, Kamil Ugurbil, and Essa
Yacoub
"Combined imaging.histological study of cortical laminar
specificity of
fMRI signals" To appear in NeuroImage 2005.
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Figure 1: Mean Diameter and Standard Deviation of
Nanoparticle Sizes
This image represents nanoparticle formation in a TiCl4
combustion simulation. Spot sizes and color on the
heated-object scale represent the mean diameter of particles at a
given point in space, while
the diversity of sizes of these spots in a given area relates
to the standard deviation of spot sizes. For example, in an area with large
particles of low deviation, we would expect mostly large spots.
In an area with medium size particles and high
deviation we would expect
to see a wider range of spots, some large and some small, whose
average size is medium.
Figure 2: Nanoparticle Count Information
Here we see a representation of nanoparticle quantities at a
point for six different sizes of nanoparticles, each
represented by a concentric ring in a target glyph. I've used six
perceptually-equiluminant colorscales to show counts of
particles of sizes 1,2,4,8,16,and 32 nanometers,
each count linked sequentially to rings of the target
glyphs from center outward. Brighter rings contain more
particles of a given size.
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Patrick Coleman Saunders, S.C. Garrick, and Victoria Interrante, Computer
Graphics Group, Department of Computer Science and
Engineering,
University of Minnesota
Figure 1 JPG
Figure 1 PNG
Figure 2 JPG
Figure 2 PNG
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A composite 'color woven' image of an experimentally acquired
particle image velocimetry dataset in which we simultaneously
highlight areas of significant positive vorticity (red), negative vorticity
(blue), strongely negative Reynolds shear stress (green), and
high swirl strength (orange or magenta, depending on the direction of the swirl).
Tim Urness and Victoria Interrante, Computer Graphics
Group, Department of Computer Science and
Engineering,
University of Minnesota
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PNG
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Figure 1:
Metal microstructure
colorized with computer software and duplicated (by
accident) by the computer and displayed as a
background pixelated image.
Figure 2:
Crystals being
precipitated from solution (with a droplet in the
center).
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Richard H. Lee, Argonne National Laboratory, retired
Figure 1 JPG
Figure 2 JPG
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Figure 1:
Figure 2:
Figure 3:
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Abstract image synthesis from geometric
principles
These examples illustrate how a small number of geometrical
principles
enables the creation of a wide range of abstract images,
ranging from
Mondrian-like juxtaposition of simple figures to complicated
textures. Most
of these principles, such as exclusion, occlusion or
transparency, are at
work in natural images formation.
Contributors:
Luis Alvarez, Universidad de Las Palmas de Gran
Canaria, Spain.
Yann Gousseau, Ecole Nationale Superieure des
telecommunications, Paris,
France.
Jean-Michel Morel, Ecole Normale Superieure de
Cachan, France.
Figure 1 JPG
Figure
2 JPG
Figure 3 JPG
The following links lead to more examples :
http://serdis.dis.ulpgc.es/ami/demos/algomo/algomo.html
and
http://www.tsi.enst.fr/~gousseau/Algomo/
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Antonello da Messina artworks depicted by Opus Vermiculatum
Mosaic Rendering
Sebastiano
Battiato, Università di Catania - Dipartimento di
Matematica ed Informatica
The underlying details are available here:
S.
Battiato, G. Di Blasi, G. M. Farinella, G. Gallo,
A Novel
Technique for Opus Vermiculatum Mosaic Rendering In
Proceedings of
14-th International Conference in Central Europe on Computer
Graphics,
Visualization and Computer Vision, WSCG 2006 Plzen, Czech
Republic,
February 2006;
Visit http://svg.dmi.unict.it/iplab/
for more info.
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PNG
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Lyapunov exponent of the logistic map with periodic
forcing
Paul
Jackway, CSIRO Australia
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TIF
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Möbius transformed square and its inverse image under stereographic projection
This is a frame from the video Möbius Transformations Revealed
Douglas N. Arnold and Jonathan Rogness, University of Minnesota
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