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Astronomers Teach a Machine To Analyze Space Images
New submitter Jim Geach writes: Our team of astronomers and computer scientists has developed a novel unsupervised machine learning algorithm — a combination of Growing Neural Gas and Hierarchical Clustering — to automatically analyze astronomical images. In effect, the algorithm performs the same task as a human 'eyeballing' an image, automatically identifying and labeling the points of interest. We're aiming to deploy the algorithm on the next generation of astronomical surveys such as LSST and Euclid where no human, or even group of humans, could closely inspect every piece of data. The algorithm could also find application in other fields, such as medical imaging and early disease diagnosis. The results are being presented at the UK National Astronomy Meeting in Wales, and the details of the algorithm are described in this paper.
That morphs into an eye.
Most astronomical discoveries are made by looking at a region of the sky for an extended period of time and then combing the pictures for differences. Even a simple diff algorithm is useful in this task.
Can they teach it to find resemblances to Waldo?
...is every scientific paper newsworthy?
> Growing Neural Gas
My doctor diagnosed me with this too.
Do you want Skynet? Because this is how you get Skynet.
Get free satoshi (Bitcoin) and Dogecoins
So this is built on the same principles of the Google image tagging algorithm that decided Blacks were Gorillas. This should prove entertaining even if it fails to be useful.
Not as difficult as it sounds
Yeah, that's what Google said.
I wonder if this algorithm will find any gorillas.
The algorithm could also find application in other fields, such as medical imaging and early disease diagnosis.
Radiologists already use software that assists in scanning images for potentially interesting features. They aren't a replacement but they apparently do a fairly good job at helping to ensure as little as possible gets overlooked. I did some consulting work in a radiology clinic some years ago and they used this technology there to good effect.
I wouldn't be surprised to see anatomic pathologists using technology like this somewhere in the future. The logistics of it are much more complicated than for radiology but I think somewhere down the line it will probably happen.
So basically a pre-scan filter that leaves all questionable findings to the experts (human) for further review.
Yes but it also serves an error checking function. Sometimes humans overlook things quite by accident and it provides as way to help ensure that an unblinking set of eyes looks things over. Sometimes these systems flag things that the doctor's miss. (and vice-versa) Both human and machine are pretty good individually but together the results are even better.
I believe these folks have been doing this for years. They even have been a participant in Google Summer of Code. They gave a presentation on how they could identify objects from cell phone pictures.
Test it on this:
http://apod.nasa.gov/apod/ap03...
Table-ized A.I.