Tag Archives: MIT

Removing reflections from photos taken through windows

MIT researchers have created a new algorithm that, in a broad range of cases, can automatically remove reflections from digital photos. On the left is the original photo taken through a window, with the photographer's reflection clearly visible. On the right, the reflection has been separated from the photo.

New algorithm exploits multiple reflections in individual images to distinguish reflection from transmission.

Larry Hardesty

It’s hard to take a photo through a window without picking up reflections of the objects behind you. To solve that problem, professional photographers sometimes wrap their camera lenses in dark cloths affixed to windows by tape or suction cups. But that’s not a terribly attractive option for a traveler using a point-and-shoot camera to capture the view from a hotel room or a seat in a train.
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Memory implantation is now officially real

Memory implantation is now officially real

Memory implantation is now officially real

The movie Inception is getting closer to reality. By planting false memories into the minds of mice, neuroscientists at MIT have created the first artificially implanted memories. And they’ve brought us closer to understanding the fallibility of human recollection.

When we experience something, say a trip to the park, a memory of the event is stored in a constellation of interconnected neurons in our brains called an “engram,” or memory trace. When you recall that trip to the park, neurons in the engram become active. Reactivate those neurons artificially, the theory goes, and you can bring the memory bubbling to the surface of someone’s psyche.

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Cells as living calculators

Cells as living calculators

Anne Trafton, MIT News Office

MIT engineers have transformed bacterial cells into living calculators that can compute logarithms, divide, and take square roots, using three or fewer genetic parts.

Inspired by how analog electronic circuits function, the researchers created synthetic computation circuits by combining existing genetic “parts,” or engineered genes, in novel ways.

The circuits perform those calculations in an analog fashion by exploiting natural biochemical functions that are already present in the cell rather than by reinventing them with digital logic, thus making them more efficient than the digital circuits pursued by most synthetic biologists, according to Rahul Sarpeshkar and Timothy Lu, the two senior authors on the paper, describing the circuits in the May 15 online edition of Nature.

“In analog you compute on a continuous set of numbers, which means it’s not just black and white, it’s gray as well,” says Sarpeshkar, an associate professor of electrical engineering and computer science and the head of the Analog Circuits and Biological Systems group at MIT

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MIT teach robot arms to think outside of the box [Video]

MIT algorithms teach robot arms to think outside of the box

Although robots are getting better at adapting to the real world, they still tend to tackle challenges with a fixed set of alternatives that can quickly become impractical as objects (and more advanced robots) complicate the situation. Two MIT students, Jennifer Barry and Annie Holladay, have developed fresh algorithms that could help robot arms improvise. Barry’s method tells the robot about an object’s nature, focusing its attention on the most effective interactions — sliding a plate until it’s more easily picked up, for example. Holladay, meanwhile, turns collision detection on its head to funnel an object into place, such as balancing a delicate object with a free arm before setting that object down. Although the existing code for either approach currently requires plugging in existing data, their creators ultimately want more flexible code that determines qualities on the spot and reacts accordingly. Long-term development could nudge us closer to robots with truly general-purpose code — a welcome relief from the one-track minds the machines often have today.