The dark orange circle in the center is myself, light blue circles are papers/presentations, light orange circles are co-authors, and dark-blue circles are co-authors of my co-authors (i.e., have not necessarily directly worked with me on a project).

Unfortunately, as of today, not all of my co-authors have Google Scholar pages, so there are a number of co-authors whose connections and branches are under-represented.  In addition, Google Scholar does not necessarily accumulate all of a given author’s papers/presentations and often makes mistakes misattributing papers to profiles.  So, the veracity of the information represented here should be taken with a grain of salt unless I find a better service for generating these networks.

For some more information on how this was created, click-through to the post.

As with the VSS DNA graph I made before the Visual Sciences Society Annual Meeting this past May, I used Python, NetworkX, and D3.js.  In addition, I took advantage of another Python module, GoogleScholar, to screen-scrape information from the Google Scholar profiles.

Starting with my Google Scholar citation profile, I loop through the individual entries and extract the titles and co-authors of each entry.  The names and titles are connected as nodes using NetworkX.  I then had a list of co-authors:

• Ari Weinstein
• Benjamin Kunsberg
• Bernard D Adelstein
• Bina Pastakia
• Chia-Chien Wu
• Chris L Baker
• David S Ebert
• E Daniel Hirleman
• Flip Phillips
• Gaurav Kharkwal
• Hong Z Tan
• Jacob Feldman
• Joshua B Tenenbaum
• Julia E. Mazzarella
• Kevin Sanik
• Kristina Denisova
• Kwangtaek Kim
• Manish Singh
• Matthew B Kocsis
• Melissa M Kibbe
• Paul Ringstad
• Peter C Pantelis
• Roger W. Cholewiak
• Roland W Fleming
• Ryan M Traylor
• Steven W Zucker
• Sung-Ho Kim
• Tim Gerstner

To create the connections, I search for the co-authors names on Google Scholar (the profiles that were used are linked above) and do the same thing, extracting the titles and (co-?)co-authors names.  This allowed me to produce a network diagram illustrating individuals who have been my co-authors, along with co-authors of those co-authors.  Many of my co-authors did not have profiles when I generated this first version and there were a few with technical problems (e.g., one profile was populated with a large number of papers from another individual with the same name as my co-author, but a different person, and pruning these problematic entries would have been labor intensive).  Still, it is a neat illustration worth sharing.

I am not currently including the code on this page because it is quite messy and “non-pythonic”, but I’m happy to share it if there is interest.  In addition, since this image was produced with D3.js, there is an interactive version of the graph available. I chose not to include it because it can be quite computationally taxing with the large number of nodes and connections and therefore not the best for directly including on the blog.

UPDATE June 20, 2014: I removed the co-author labels from the lead image because I don’t want to give the false impression that specific co-authors are better connected than others.  Since this visualization is dependent on a 3rd party scraping service, it is problematic to draw any conclusions about “connectedness” from this representation.

As you can see, there are large numbers of abstracts that have few shared authors.  Those abstracts that share authors often join together to create “chains” of students, advisors, and colleagues.

This is a first version, hastily pulled together, so there are a few problems.  The nodes are assigned to authors by name, which can be a problem for authors sharing the same name (which creates more connections than appropriate for a given node) or who  have inconsistent reporting of their name (for example, omitting the middle initial or alternate spelling, which can create another erroneous node). I am thinking of addressing the duplicate node issue by using a string similarity metric (e.g., Levenshtein distance) to find strings that contain similar names to combine the connections, but this could be an issue if the names are truly different people. Alternatively, I could incorporate the authors’ affiliations, but this carries similar issues (e.g., I report my affiliation as “University of Giessen” while colleagues report it as “Justus-Liebig-Universität Gießen”).

Although there are lingering issues, it is still an interesting illustration of the connections between the different abstracts being presented at VSS 2014.

Here’s the code on GitHub: visvssrelationships

So the question is: What do I need to do to get this 3D object printed at Shapeways? Click through to see the steps that I took to get this 3D model printed economically.

Since the object generation script creates a MATLAB struct with vertices and faces, I was able to use the vertface2obj script by Anders Sandberg to export .obj files. Here is the object at a variety of resolutions in .obj format:

• 162 vertices (12 KB)
• 642 vertices (41 KB)
• 2562 vertices (160 KB)
• 10242 vertices (659 KB)
• 40962 vertices (2.8 MB)

These .obj files can be imported directly into Shapeways. After importing, Shapeways runs a series of sanity checks to make sure that the file can be printed and then posts it on their site. Here’s a link to one such imported object.

Notice the price? (~$63 for a plastic print) Whoa! That’s a lot more than I wanted to pay for a simple blob model.

It turns out that the cost of printing a model at Shapeways is proportional to the model’s volume (source):

Our pricing is based upon the actual amount of material used in your product and the material you choose to use. So, the actual volume of your finished product not the volume of the bounding box determines the price.

So, the way to print this shape economically is to reduce the volume of the print. Shapeways suggests a couple of ways to reduce the cost of the 3D print, including hollowing out the model and carving it to reduce the total surface area. Using OpenSCAD, I was able to do both.

OpenSCAD allows for the importation of .stl files, which is another “standard” 3D file format. Using the stlwrite script, I was able to export my faces/vertices from MATLAB to a file that could be imported into OpenSCAD. Here’s the previously illustrated example file at a variety of resolutions in .stl format:

• 162 vertices (16 KB)
• 642 vertices (66 KB)
• 2562 vertices (258 KB)
• 10242 vertices (1 MB)
• 40962 vertices (4.1 MB)

Let’s work with the 10242 vertices file, “0431630057_sphere_tri_5.stl“. First, we can import it into OpenSCAD using:

Then we can do a “difference” between the object and a downscaled version to create the shell using (note that the % “background modifier” is used to make the bounding object transparent gray):

So we now have a shell, but let’s get rid of some more of the material by carving out a lattice of packed spheres. First, we will create a lattice of spheres (I just used the description on the Wikipedia page to create some simple procedural generation code):

Now we subtract the lattice from the shell that was previously generated, again using the difference command:

This would normally be the end of the process and you could then save the .stl and import it into Shapeways. However, all of the OpenSCAD coding turned out to be a useless garden path because the OpenSCAD computation took far too long and too much memory to process when using higher resolution base shape files and higher resolution spheres (repeatedly crashing when I tried to compile the code).

So, I tried an alternative route… Blender. Let’s replicate the steps above in Blender’s Python scripting interface. First, import0431630057_sphere_tri_5.stl into Blender:

Then, duplicate the object, reduce its scale, and take the boolean difference between the copy and the original to create a shell:

Then we create a lattice of spheres:

And finally, take the boolean differences between the shell and the spheres. Putting it all together:

This workflow uses up a lot less memory than the OpenSCAD route, but the processing time is still painfully slow. Thankfully though, it does not crash the compiler. To monitor the progress of the process, I simply run the script from the command line using “blender –background –python ‘SCRIPTLOCATION'”. I used the same bit of code as described above to create the “production quality” model, but used a higher resolution base shape (0431630057_sphere_tri_6.stl), higher resolution spheres (sphereSubdivisions = 5;), and a higher spatial resolution lattice (r = 0.1;). I saved it using:

After importing the object at Shapeways, we see that we can save a serious chunk of change on printing the new, modified model. Here is a link to the new and improved product on Shapeways, now only $3.66 (vs. $63.54) for the White Strong & Flexible Material.

The object on the left in the above video is a textured and shaded object with a small amount of specular reflection (lit using the Debvec Funston Beach at Sunset light probe). On the right, I’m illustrating the dominant orientations in the image, across the surface of the object.

Click through for some more visualizations.

The above video illustrates the vector field’s dominant orientations as well as the kurtosis/”peakedness” of the orientated filter response distributions (reflected in the size of the vectors).

Using line integral convolution, we get a better visualization of the global structure of the orientation field. Note that the above LIC images were computed in MATLAB using the Matlab Vector Field Visualization toolkit.

The LIC images in the video above were computed in Mathematica and colored as a function of the orientation field’s dominant orientation.

Finally, a video illustrating all the aforementioned visualization techniques simultaneously.

Cholewiak, S. A., Fleming, R. W., & Singh, M. (2013). Visual perception of the physical stability of asymmetric three-dimensional objects. Journal of Vision, 13(4), 1–13. doi: 10.1167/13.4.12

Here’s the abstract:

Visual estimation of object stability is an ecologically important judgment that allows observers to predict the physical behavior of objects. A natural method that has been used in previous work to measure perceived object stability is the estimation of perceived “critical angle”—the angle at which an object appears equally likely to fall over versus return to its upright stable position. For an asymmetric object, however, the critical angle is not a single value, but varies with the direction in which the object is tilted. The current study addressed two questions: (a) Can observers reliably track the change in critical angle as a function of tilt direction? (b) How do they visually estimate the overall stability of an object, given the different critical angles in various directions? To address these questions, we employed two experimental tasks using simple asymmetric 3D objects (skewed conical frustums): settings of critical angle in different directions relative to the intrinsic skew of the 3D object (Experiment 1), and stability matching across 3D objects with different shapes (Experiments 2 and 3). Our results showed that (a) observers can perceptually track the varying critical angle in different directions quite well; and (b) their estimates of overall object stability are strongly biased toward the minimum critical angle (i.e., the critical angle in the least stable direction). Moreover, the fact that observers can reliably match perceived object stability across 3D objects with different shapes suggests that perceived stability is likely to be represented along a single dimension.

Want to cite us? Click through for the BibTeX source.

[code]@ARTICLE{Cholewiak2013,
author = {Cholewiak, Steven A. and Fleming, Roland W. and Singh, Manish},
title = {Visual perception of the physical stability of asymmetric three-dimensional
objects},
journal = {Journal of Vision},
year = {2013},
volume = {13},
pages = {1–13},
number = {4},
abstract = {Visual estimation of object stability is an ecologically important
judgment that allows observers to predict the physical behavior of
objects. A natural method that has been used in previous work to
measure perceived object stability is the estimation of perceived
“critical angle” — the angle at which an object appears equally
likely to fall over versus return to its upright stable position.
For an asymmetric object, however, the critical angle is not a single
value, but varies with the direction in which the object is tilted.
The current study addressed two questions: (a) Can observers reliably
track the change in critical angle as a function of tilt direction?
(b) How do they visually estimate the overall stability of an object,
given the different critical angles in various directions? To address
these questions, we employed two experimental tasks using simple
asymmetric 3D objects (skewed conical frustums): settings of critical
angle in different directions relative to the intrinsic skew of the
3D object (Experiment 1), and stability matching across 3D objects
with different shapes (Experiments 2 and 3). Our results showed that
(a) observers can perceptually track the varying critical angle in
different directions quite well; and (b) their estimates of overall
object stability are strongly biased toward the minimum critical
angle (i.e., the critical angle in the least stable direction). Moreover,
the fact that observers can reliably match perceived object stability
across 3D objects with different shapes suggests that perceived stability
is likely to be represented along a single dimension.},
doi = {10.1167/13.4.12},
eprint = {http://www.journalofvision.org/content/13/4/12.full.pdf+html},
file = {Article:http:\semifluid.com\wp-content\uploads\2013\\03\Cholewiak-Fleming-Singh-2013-Visual-perception-of-the-physical-stability-of-asymmetric-three-dimensional-objects.pdf:URL},
owner = {Steven A. Cholewiak},
url = {http://www.journalofvision.org/content/13/4/12.abstract}
}
[/code]

I created a series of HDR cube maps using NVIDIA’s CubeMapGen (currently hosted on Google Code). Starting with the Debevec light probes, I applied a Gaussian blur with increasing kernel size (10°, 20°, 30°, 40°, and 50°), creating 6 cube maps (one for each blur). In the videos, the cube maps have increasing blur from left-to-right, top-to-bottom. Note that I did not tone-map or account for changes in overall exposure (so the specular reflections can appear blown-out, especially for the higher blurs). After the break, you can see the effect using different light probes (and different shapes).

Using the OpenGL/Psychtoolbox framework I have previously described, I replicated this interesting effect. When you play the following movie, a 3D sphere (with sinusoidal perturbations) is rotated. Note the axis of perceived rotation when the object has specular reflections (1st half of the movie) and when the environment map is “painted” onto the surface (2nd half of the movie).

The physical motion of the object is the same in both cases — the object rotates around the vertical axis. When the object only has specular reflections, it appears to rotate around an oblique 45° axis, but when textured it rotates around the vertical axis. After the break, I show similar effects when the object is rotated around the horizontal axis, 45° axis, and when the spatial frequency of the perturbation is manipulated.

Here is the same object, now rotated around the horizontal axis. We have the same perceived effect, rotation around the oblique 45° axis when only specular reflections are present.

Here is the same sphere, now rotated around an axis 45° off-vertical. Note that the motion for the specular case appears the same when the object is rotated around the vertical and horizontal axis (as shown above) or around the 45° axis (shown below). However, the motion is clearly different when the object is textured.

Neat, right?

Now, here’s a series of videos illustrating the effect for perturbations with different spatial frequencies. Note the changes in perceived object geometry and axis of rotation for low and high frequency perturbations.

2 8 more videos (with different shapes and illumination conditions) after the break.