Wednesday, February 21, 2007

Biologically Inspired Vision Systems

Continued from page 1

By Duncan Graham-Rowe

To test their theory, Serre and Poggio worked with Stanley Bileschi, also at MIT, and Lior Wolf, a member of the computer science department at Tel Aviv University in Israel, to create a computer model comprising 10 million computational units, each designed to behave like clusters of neurons in the visual cortex. Just as in the cortex, the clusters are organized into layers.

When the model first learns to "see," some of the cell-like units extract rudimentary features from the scene, such as oriented edges, by analyzing very small groups of pixels. "These neurons are typically like pinholes that look at a small portion of the visual field," says Serre. More-complex units are able to take in a larger portion of the image and recognize features regardless of their size or position. For example, if the simple units detect vertical and horizontal edges, a more complex unit could use that information to detect a corner.

With each successive layer, increasingly complex features are extracted from the image. So are relationships between features, such as the distance between two parts of an object or the different angles at which the two parts are oriented. This information allows the system to recognize the same object at different angles.

"It was a surprise to us when we applied this model to real-world visual tasks and it competed well with the best systems," says Serre. Indeed, in some tests their model successfully recognized objects more than 95 percent of the time, on average. The more images the system is trained on, the more accurately it performs.

"Maybe we shouldn't be surprised," says David Lowe, a computer vision and object recognition expert at the University of British Colombia in Vancouver. "Human vision is vastly better at recognition than any of our current computer systems, so any hints of how to proceed from biology are likely to be very useful."

At the moment, the system is designed to analyze only still images. But this is very much in line with the way the human vision system works, says Serre. The inputs to the visual cortex are shared by a system that deals with shapes and textures while a separate system deals with movement, he says. The team is now working on incorporating a parallel system to cope with video.