MIT has developed a customized camouflage generator that can hide just about anything within a box-shaped container.
Researchers generated algorithms based on photos taken of a synthetic scene. For each camera angle, the algorithms produced a set of covers for the object’s visible faces so it could blend in with its surroundings.
Much like we draw an ellipses in a flat circle to perceive it as a sphere, these algorithms work to reduce the differences between what’s perceived by the viewer and what’s patterned in the camouflage. A pattern that works well for one angle would stick out in another. The algorithms can compensate by averaging the color values of each face from each camera angle, as seen below.
These algorithms were then tested on volunteers who logged how long it took to notice hidden objects within the scene. On average, the best performing ones took over three seconds for participants to detect the object. This average fares much better than standard camouflage, which primarily works to avoid casual glances.
“They use texture-synthesis techniques that are pretty well known in the community,” said James Hays, an assistant professor of computer science at Brown University. “They’ve been used for things like photo editing, but never for designing a real-world artifact.”
These findings will be presented at the 2014 Computer Vision and Pattern Recognition conference in Columbus, OH.
One of the things the camouflage generator doesn’t account for yet are lighting and climate changes. Taking this technology to the next level would require the Camouflage to change like a chameleon and be adaptive to its environment. Speaking about this issue, Hays says “the same methodology could be extended,” and that “this general idea will be useful regardless of how you generalize the problem.” Once accounted for, Camouflage on par with the Invisibility Cloak won’t be too far away!
About the author
Cory Healy is a freelance writer based in Brooklyn. When he's not writing about tech or music, he can either be found delivering food on bike throughout lower Manhattan, or playing computer games at home.
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