This Software Flags "Inappropriate" Google Glass Photos

Don't want Glass snapping pictures while you're in a public restroom? Meet PlaceAvoider, a new image-detection system being developed at Indiana University.

Google Glass, which just got 200% less dorky looking now that it affixes to prescription lenses, is still at a point where it raises more questions than it answers. And one of those questions is: When is Glass inappropriate to wear?

The answer might seem obvious to you, Person with Common Decency, but for others who might have a tougher time picking up obvious social cues, the etiquette of the future might not be so straightforward.

Enter a new software technology called PlaceAvoider, which is currently being developed by imaging experts at Indiana University. Its goal is to take out a lot of the guesswork for would-be photographers by automatically blacklisting images taken in inappropriate settings--accidental or otherwise. Apu Kapadia, who co-created the system, tells MIT Technology Review that the project aims "to help people exploit these applications to the full by providing them with a way to share safely."

How does it work? Well, it doesn't totally--at least not yet. (You can read the paper in PDF form here.) But the general idea is that PlaceAvoider uses algorithms to gauge your location using a system called scale-invariant feature transform (SIFT). That's a fancy name for something that automatically susses out where you are based on a photo. Certain rooms can be blacklisted by the user. For example, the imaging software can be trained to recognize when you're inside a public bathroom versus when you're out walking your dog. If you use Glass to snap a photo in a place deemed sensitive--restrooms, movie theaters, board meetings, etc.--that image is flagged or quarantined.

In reality, it isn't so different from some of the facial-recognition systems we've already seen, only instead of scanning for human facial features, PlaceAvoider's algorithms scan for toilets. And so far, at least, the technology's future looks promising: The prototype made it through the initial round of image guesswork with 89.8% accuracy.

[Image: Wikipedia]

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