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In connection with the 2005-2006 program on Imaging at the Institute for Mathematics and its Applications, we have created this web gallery of contributed images. For this we solicited images for their visual rather than scientific interest. Each image can be viewed as a JPEG file and in the original contributed format. Rights to the images are retained by their owners. To contribute an image for inclusion, see the instructions.
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Chaotic CT reconstruction.
Alex Zamyatin, Bio-Imaging Research JPEG TIFF |
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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 JPEG TIFF |
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Seismic reflection image of a vertical slice
through the upper 3 km of the Earth's crust.
Kidane Araya JPEG PostScript |
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Electromagnetic wave in the Fujisawa-Koshiba photonic crystal with a waveguide bend.
Igor Tsukerman JPEG |
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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. JPEG BMP |
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The depth of brain sulci in axial, coronal and sagittal
view
Chiu-Yen Kao, IMA JPG PNG |
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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. |
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. JPG 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. |
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. JPG BMP |
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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 JPG |
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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. |
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 1 JPG Part 2 JPG |
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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. |
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 TIF |
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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. JPG PNG |
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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). JPG 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). JPG PNG |
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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 JPG PNG |
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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. JPG 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. JPG PNG |
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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. JPG PNG |
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Fourier basis functions of Japan (lowest
25 "frequencies")
Naoki Saito, Department of Mathematics, University of California, Davis JPG PS 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. JPG |
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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 JPG |
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a. Computer generated stippling rendering of the Mount Rushmore
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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 b.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 JPG |
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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 JPG TIF |
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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, JPG |
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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.
There exists two Moebius bands on a Klein bottle. The second
band is symmetric to the shown Moebius band.
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Konrad Polthier, Zuse Institute Berlin (ZIB)
http://www.zib.de/polthier/images
Figure
1: JPG |
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Figure 1: Height 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
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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 JPG PNG |
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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 JPG TIF |
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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. JPG |
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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.
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. |
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 JPG 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.
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Richard H. Lee, Argonne National Laboratory, retired
Figure 1 JPG Figure 2 JPG |
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Figure 1: |
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.
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: Visit http://svg.dmi.unict.it/iplab/ for more info. |
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Lyapunov exponent of the logistic map with periodic
forcing Paul Jackway, CSIRO Australia JPG 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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