We already see the world around us through the windows of our home or the windshield of our car, so … why not just use those things as camera lenses, instead of utilizing separate security or obstacle-avoidance cameras? Thanks to a new system created at the University of Utah, that could be a possibility.

Developed by a team led by associate professor Rajesh Menon, the demo model of the system consists of a plexiglass window with an ordinary digital camera image sensor connected to one side, so that it points into the edge of the plexiglass (real glass would also work). Facing that window is an LED light board, on which simple still and animated images are displayed.

While most of the light from the LED board passes right through the window, about 1 percent of it is scattered through the plexiglass, moving out to the edges. Reflective tape placed on those edges reflects the scattered light back in, allowing it to be picked up by the sensor. A custom algorithm on a connected computer is then used to analyze and "decode" the sensor data, transforming what would otherwise be a meaningless mess into a low-resolution but recognizable full-color reconstruction of the light board image.

Although the image quality isn't up there with that of a regular camera, it's sufficient for industrial applications – along with the previously-mentioned security and obstacle-avoidance systems, these applications could also include things like low-profile eye-tracking cameras in augmented reality goggles or iris scanners.

"Why don't we think from the ground up to design cameras that are optimized for machines and not humans?" says Menon. "That's my philosophical point."

Additionally, he notes that more powerful sensors should produce higher-resolution images. His team is now refining the system, which includes making it capable of producing images using ordinary household light that's reflected off objects, as opposed to light that's projected by an LED board.

A paper on the research was recently published in the journal Optics Express. You can see examples of what the technology is capable of, in the video below.

Source: University of Utah