“Color is used as one of the principle components in recognizing ‘good’ from ‘bad,’ ‘go’ from ‘no-go,’ so there’s a huge implication here for a variety of industrial uses,” Kar says.

Machines typically recognize color by breaking it down, using conventional RGB (red, green, blue) filters, into its constituent components, then use that information to essentially guess at, and reproduce, the original color. When you point a digital camera at a colored object and take a photo, the light from that object flows through a set of detectors with filters in front of them that differentiate the light into those primary RGB colors.

You can think about these color filters as funnels that channel the visual information or data into separate boxes, which then assign “artificial numbers to natural colors,” Kar says.

“So if you’re just breaking it down into three components [red, green, blue], there are some limitations,” Kar says.

Instead of using filters, Kar and his team used “transmissive windows” made of the unique  two-dimensional material.

“We are making a machine recognize color in a very different way,” Kar says. “Instead of breaking it down into its principal red, green and blue components, when a colored light appears, say, on a detector, instead of just seeking those components, we are using the entire spectral information. And on top of that, we are using some techniques to modify and encode them, and store them in different ways. So it provides us with a set of numbers that help us recognize the original color much more uniquely than the conventional way.”

“As the light pass through these windows, the machine processes the color as data; built into it are machine learning models that look for patterns in order to better identify the corresponding colors the device analyzes”, Ostadabbas says.

“A-Eye can continuously improve color estimation by adding any corrected guesses to its training database,” the researchers wrote.

For more information: www.northeastern.edu