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.

This post marks the release of version 1.0 of our polyline unwrap plugin. As described in the previous post, this tool helps you position polyline masks more accurately by obtaining lines of samples at intervals along the length of the polyline. Think of it like a ribbon of sample points tracking the polyline, which is then laid flat as a single image strip. It is used in-house to quickly visualize and correct deviations from the ideal edge across multiple frames. As an example, footage of a cigarette burning was masked and linked to the unwrap tool (see image on the right).

This example video shows a yucky little roto problem…  Time lapse footage of a shape variant object. The colors flicker like mad, the shape is doesn’t interpolate well with few keyframes, there’s nothing for a point tracker to grab onto…  It’s just the sort of thing you need to use a spline on.

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This week we’re talking about a tool that helps you position polyline masks more accurately.  The following diagram shows the idea we are exploring. Basically, perpendicular lines of interpolated samples are obtained at intervals along the length of the polyline, which then become the columns of the output image.

Sampling along polyline to generate output image

When isolating a object with a definite edge, the ideal output is a horizontal line at the exact center of the image, dividing the interior and exterior textures. The next three images show the tool in action on an image of Morning Glory Pool at Yellowstone National Park. This image has a high-contrast yellow-to-green edge along the bottom of the polyline (foreground of the image) and a yellow-to-gray edge along the top.

The next image shows what is happening conceptually. Interpolated samples are obtained along lines that are perpendicular to the polyline. The number of samples between key points is consistent, so the sampling is finer when the key points are closer to one another.

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Here’s a little attempt at making a 3D vectorscope using particles.  It’s cute like the centroid comp, elegant and simple with no plugins or fuses needed.   Nothing wrong with fuses or plugins, it’s just neat to be able to have something that works for any using Fusion 5.2 or later without anything else, and lets you see what Fusion can do out of the box.

3D Vectorscope Fun!

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