We’ve been working on some new tools for automated segmentation, and more importantly, rationalization.

Matt shared with me a bit on the method he used in his thesis for finding the centroid of an image sample.   I made an attempt at producing such an effect solely with the tools built into Fusion, and managed a pretty nice result that’s not too shabby on the speed.  I suspect it will be sped up a lot when we make it into a fuse or plugin, but the effect is nice just as a comp, as it speaks visually to a concept that doesn’t sound like it would be visual at all.

In this first video, the green crosshair is finding the barycenter of the various circles, taking their size, density, and softness into account.

In the next video, the same thing is happening, this time on a particle system.  Ben thought the circle example was silly because circles are easy to solve for, and this method works per-pixel, so I wanted to show an example of that type of setup.

And here’s a comp to try out on your own.

Barycenter_Example_C’_B01

This might be a rare acceptable usage of the bad x-ray look.  Along with the font, the music, and even the host (sorry, Mr. Hess) and presentation format,  the x-ray skull and brain graphic make The Knowledge Chamber look like a cheesy show from the early nineties.  But it’s not pretending to be an x-ray.  It is, however, a testament to how visualization techniques are often associated with an era.  Some of these are fads, and some are artifacts of the limitations of the technology at the time.  One hopes that when the limitations are lifted, the use of these visuals are deliberate design decisions.

Also, Congrats to Johnny Lee on your new position at MSR.

Ben Lipman

In part one, I talked about the terrible technique used to fake the look of an x-ray image.  Now that I’ve snatched away an item from your bag-of-tricks, it’s only fair to replace it with a new technique. But before I do that. Let’s explore how x-ray imaging works.

Simple Definition

This is the standard illumination model, or how our eyes and cameras work. This is actually a tiny sub-set but you get the idea. Light sources emit energy that bounces off objects into a camera and absorbed by a sensor. This could be film or a CCD on a digital camera.  Actually the purpose of the camera isn’t so much collecting the light from the image, rather the camera is blocking the light that is not the image from exposing the film.  Light is coming from all directions and bouncing all over (not shown), but only the light that converges through the lens is captured.  This is not the case for x-rays. The whole point of x-ray imaging is to have the energy mostly penetrate the object and measure the how much reaches the sensor. This type of imaging is analogous to cast shadows.

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Trying out some new datasets and new techniques…

EDIT:  Jim asked for some more details, and I already had some images that I intended to post, but forgot about.  So here’s a breakdown of the three layers used to make the above image…

Chameleon rendered layers

The left layer is an environment map lookup, the middle is a front lit with high opacity, and the right is a backlit with low opacity.   These were then additively composited together.

I also did some tests on this dataset with clipping.

Chameleon culled with spherical gizmo

Chameleon culled with box

The box culling was an accident, but I thought it looked like a cut of meat that had been chewed on by mice.

Here are some results showing noise reduction of raytraced sources.   It’s an improved revision of the experiments we tried previously... The first movie is basically a split showing the original and the noise reduced result.  It works best to play it full screen, at low quality (which should disable any post filtering).  Unfortunately, the compression used to make this playable over the internet tends to minimize the noise, so you have to look very closely.

Note: these are larger than most of our downloads because the compression is mild.

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