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Although this method can achieve limited results, it does not represent the way the brain processes images. Neurophysiologists at the CBCL are studying how, exactly, the brain does its visual work. They note how each pixel in an image stimulates a photoreceptor in the eye, for instance, based on the pixel’s color value and brightness: each stimulus leads neurons to fire in a particular pattern.

The programmers make a mathematical model of those patterns, tracking which neurons fire (and how strongly) and which don’t. They tell the computer to reproduce the right pattern when it sees a particular pixel, and then they train the system with positive and negative examples of objects. This is a tree, and this is not.

But instead of learning about the objects themselves, the computer learns the neuron stimulation pattern for each type of object. (Essentially, it’s learning patterns of patterns: the patterns of neural reactions not just to pixels but to groupings of pixels.) Later, when it sees a new image of a tree, it will see how closely the resulting neuron pattern matches the ones produced by other tree images. Poggio says this is similar to the way a baby’s brain gets imprinted with visual information and learns about the world around it.

The researchers applied standard tests to the system and found that it can detect people and cars in a street scene about 95 to 98 percent of the time, Bileschi says. The system doesn’t just identify objects; it can view stills or video and recognize an action. It might recognize running, for instance, based on how a leg is bent or how quickly a person shifts position from one frame to the next.

David Lowe, professor of computer science at the University of British Columbia, says many researchers in the field of object recognition had believed that limiting the computer to “biologically plausible” calculations would reduce the machine’s performance compared with approaches that didn’t limit the functions a programmer could include. “However, the latest work by this group has produced some of the best results on standard object classification experiments,” he says. Even Poggio was surprised: he’d thought vision was too poorly understood for researchers to successfully mimic the brain.

“These are first-rate people, and they have some of the best technology for learning and recognizing objects,” says Pietro Perona, a computer vision expert at the California Institute of Technology. But turning the technology into a marketable product, he says, will still take some work.

Bileschi and Lior Wolf, a postdoc in Poggio’s lab, recently presented their work to a conference at MIT’s Deshpande Center for Technological Innovation, along with students from MIT’s Sloan School of Management. They’re hoping to attract interest from someone who will help fund the development needed to take this research from the lab into the marketplace. Although he’s not sure what the best application of the technology would be, Bileschi feels certain that it’s mature enough to be marketable. “It’s no longer a neat toy that does something interesting most of the time,” he says. “It’s at the point where it does something useful almost all of the time.”

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