Slashdot Mirror
The Math of a Fly's Eye May Prove Useful
cunniff writes "Wired Magazine points us to recent research that demonstrates an algorithm derived from the actual biological implementation of fly vision (PLoS paper here). Quoting the paper: 'Here we present a model with multiple levels of non-linear dynamic adaptive components based directly on the known or suspected responses of neurons within the visual motion pathway of the fly brain. By testing the model under realistic high-dynamic range conditions we show that the addition of these elements makes the motion detection model robust across a large variety of images, velocities and accelerations.' The researchers claim that 'The implementation of this new algorithm could provide a very useful and robust velocity estimator for artificial navigation systems.' Additionally, the paper describes the algorithm as extremely simple, capable of being implemented on very small and power-efficient processors. Best of all, the entire paper is public and hosted via a service that allows authenticated users to give feedback."
After presenting his paper, researcher David O'Carroll strode off the stage and into a sliding glass door.
-Peter
The researchers drew their algorithm from neural circuits attuned to side-to-side yaw, but O’Carroll said the same types of equations are probably used in computing other optical flows, such as those produced by moving forward and backwards through three-dimensional space.
I vaguely remember seeing a study that examined how bees travel without hitting anything but using very few neurons. Something about the relative size change of objects between eyes. They tested this by putting bees in a clear tunne with patterns on belts on the right and left walls. By changing the speed of the belts, the bees would ram into the walls, but as long as the belts were moving at the same speed, the bees were fine. Is this ringing a bell for anyone else?
Look, you've got to at least RTFS.
You'd be surprised. About medicine.
Starting with simple Aspirin, the line of medicaments that have known positive results but nobody knows why, is loooooooong.
I find it unimaginable that people would attempt to implement a technology that is not fully understood. Doing so will eventually yield unexpected results or at the very least, results that cannot be explained.
Except that occasionally building a working model is a useful step to understanding it. I'm happy that Edward Jenner in 1796 started infecting people with cow pox as a way to prevent small pox even though he didn't fully understand why it worked.
Well, it's not flying airplanes full of people. They've copied a system that seems to work well, and they are testing it for ultra-small UAVs. They'll get a better idea of how it works then.
There are many things people have used throughout history without understanding how they work. Salt preservation is ancient, but we didn't discover the bacteria it kills until the last 200 years.
Sometimes, "works" is good enough. And works often leads to understands.
I think this is really cool. We've been trying to do this kind of thing for years. The fly does it with a tiny-micro-fraction of the resources we were using, and does a much MUCH better job. By testing this system, perhaps we'll find out WHY our systems are tricked by certain stimulus and this one isn't.
It's not like they put a bunch of stuff together and said "this works, as far as we can tell", they took a proven system and copied it.
Comment forecast: Bits of genius surrounded by a sea of mediocrity.
A scientist says, "This works, but I don't know why, How do I complete the theory?"
An engineer says, "This works, but I don't know why. How do I use it to build something that does what I want?"
A good engineer says, "This works, but I don't know why. How do I use it to build something that does what I want.. And, in what domain does my model break down and how do I make sure I don't get my system into that domain?"
Sizable chunks of control theory, frequency analysis, and some other core theoretical components of what we now consider to be solid engineering work were being applied long before the theoretical basis behind them was solidly proven to be correct from a pure mathematical standpoint.
But where's the source code???
Aspirin's mechanism of action was unknown for a long time, but now we know it's due to suppression of prostaglandin synthesis by irreversible inhibition of cyclooxygenase enzymes.
Give me Classic Slashdot or give me death!
And if God had intended for Man to drink beer we would have been born with stomachs.
We've implemented this algorithm in several autonomous flying surveillance vehicles. While it appears to work adequately, we're still trying to determine why the only thing they manage to locate is cow shit.
Have gnu, will travel.
This might be offtopic but seeing a legitimate R&D story on slashdot with a link to the actual (open) technical write up of the research made my day. I haven't read the whole paper yet (I will when I get home) but going through it and reading the first few sections I can see that the researchers included their (simulink?) processing models as well as some good data in the results section. This story finally gave me something worth breaking out my old signal processing and DAC notes from college out over and studying the raw math and theory behind the algorithm.
I have to say, I really wish we would see more papers like this posted and published openly. It's very inspiring when other folk in similar fields can access a paper's full contents and start playing with similar models themselves...
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