A sizable segment of the population suffers from color blindness, enough so that it’s worth considering the implications on color palettes and usability.  This tool allows you to simulate the ways that various color vision deficiencies will affect you imagery.  I’ve noticed that some of the images we create probably won’t read very well to some people, and this easily lets us check if we’ve created something that could be ambiguous.

ColorBlind fuse

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Fusion 6 added a Color Matrix tool that lets you enter your own matrix by hand, but the biggest problem with it is the lack of any methods to modify it with.  You can’t even assign controllers to it.

Fuses, however,  let you use handy methods to modify a matrix.  I’ve used some of them to create an RGB equivalent of the 3D Transform tool.  It has a similar UI, just as 3TT does, but this modifies RGB, not XYZ or UVW.

Color Matrix Transform fuse

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Two more Fuses, this time some really simple ones that convert multi-channel images to mono-channel and back again.   Color is overrated, in general, and I find myself getting a lot of use out of these.

Color to Mono

Mono to Color

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We’ve seen some pretty cool things at SIGGraph so far…

Gel Sight

Gel Sight is a retrographic surface imaging technique that was wonderfully elegant in it’s simplicity and effectiveness.  They also gave out free samples…

Cuda raycasting 13GB of cryomacrotome goodness (in stereo)

Nvidia had a stereographic interactive realtime rendering of the full 13GB Visible Human dataset being rendered in CUDA on 3 Quadroplexi.  Very impressive.  The glasses used were the new Nvidia active shutter glasses, and were very effective.

Resistive multitouch in many form factors

A new startup out of NYU showed a novel resistive multitouch device.  Very effective, low cost, and suitable to many applications.

UPDATE: Sorry about the broken link, Touchco was bought up by Amazon, so pretty much all of the cool applications they had in mind are replaced by the Kindle 3.

VLC madness courtesy 2 Fusion-io cards

Fusion-io showed their new “budget” nonvolatile storage adapter, the ioXtreme.   $900 gets you 80GB, with a read speed o 700MB/s.  The IO’s aren’t very high, much less their enterprise solutions, but that doesn’t matter if you are reading sequential data.  The booth was pretty crazy, too, one of the better live hardware demos I’ve seen in a while.  I’ll get some pictures tomorrow.  VLC never looked so impressive…

We’ve been adding CUDA support to many of our custom tools lately and finding speed-ups of 40x in some cases. This tool is an example of an idea that would be totally impractical (hence the krazy) without a highly parallel GPU-based approach. The algorithm takes each pixel in the input image, and finds the shortest distance (in RGB space) from it to the pixels in the “selection” image. This requires an every-pixel-to-every-pixel distance calculation, with the final shortest distance value compared to a threshold to determine whether or not to mask the pixel.

Download KrazyKey 1.0

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I was working on a little job today with a 2D temporally variant scalar field.

You know, B&W footage.

I needed to find the parts of the data that were changing the most and compare them to the overall data and the maximum delta.

What I ended up with, once Ben pointed it out to me, was a simple example of calculus laid out in a couple tools.   The simplest case is just taking the frames I have and interpolating the same number of frames, so there’s no missing samples.  It’s silly, really.

But you can try it with other sampling, so there’s also an example of a Sobel filter, with a 1D kernel perpendicular to the normal 2D one.  Cute really.

If you checked out my interactive smoothing comp, you can see how I used a Sobel filter to make the forward facing laser pointer by looking at the differentiation of the R and G channels over time.  Same idea, just different way of expressing the temporal dimension.

I’m tossing in a Laplacian filter too, just for fun, it’s not useful for the calculus part, but it was easy to do, and shows how you can change the kernel to make different effects.  It’s possible to also evaluate 2D or 3D kernels this way, too.  The temporal offsets can be combined with spatial offsets so you could make a 3D blur filter, or a 3D sharpen.  Or a 3D Unsharp Mask, as I’ve also included.

We were recently commissioned to do some visualizations for a virtual colonoscopy procedure.   Also known as a colonography.   If you’re unfamiliar with the process, Wikipedia has enough information to give you a general overview. While there is a lot of information on the internet about the scanning process itself and what the patient will experience, there isn’t much about the ways in which the CT data can be analyzed once it is acquired.  So here’s a brief overview of what we at Anatomical Travelogue did with it.

Screengrab from the realtime colonoscopy demo

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We were recently commissioned to do some visualizations for a virtual colonoscopy procedure.   Also known as a colonography.   If you’re unfamiliar with the process, Wikipedia has enough information to give you a general overview. While there is a lot of information on the internet about the scanning process itself and what the patient will experience, there isn’t much about the ways in which the CT data can be analyzed once it is acquired.  So here’s a brief overview of what we at Anatomical Travelogue did with it.

Screengrab from the realtime colonoscopy demo

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Another in what is turning into a series of posts where standard Fusion tools are transformed into something very interesting…

I’m smoothing animation data (or adding noise or offsets or whatever) using nothing more than some Probes.

In this video, the green dots represent the original animation, the blue dots the smoothed animation, and the orange is the original with some noise added.