Applications include surveillance, visual search engines, biomedical imaging analysis and robots with realistic vision.

For the first time, MIT scientists have applied a computer model of how the brain processes visual information to a complex, real world task: recognizing the objects in a busy street scene. The researchers were pleasantly surprised at the power of this new approach.

"People have been talking about computers imitating the brain for a long time," said Tomaso Poggio, the Eugene McDermott Professor of Brain and Cognitive Sciences and a member of the McGovern Institute for Brain Research at MIT. "That was Alan Turing's original motivation in the 1940s. But in the last 50 years, computer science and AI (artificial intelligence) have developed independently of neuroscience."

"Our work is biologically inspired computer science," said Poggio, who is also co-director of the Center for Biological and Computational Learning.

"We developed a model of the visual system that was meant to be useful for neuroscientists in designing and interpreting experiments, but that also could be used for computer science," said Thomas Serre, a postdoctoral associate in Poggio's lab and lead author of a paper on the work in the March 2007 IEEE Transactions on Pattern Analysis and Machine Intelligence.

"We chose street scene recognition as an example because it has a restricted set of object categories, and it has practical social applications," said Serre.

Near-term applications include population surveillance and assistance for automobile drivers; eventually, applications could include visual search engines, biomedical imaging analysis and robots with realistic vision. On the neuroscience end, this research is essential for designing augmented sensory prostheses, such as ones that could replicate the computations carried by damaged nerves from the retina.

"And once you have a good model of how the human brain works," Serre explained, "you can break it to mimic a brain disorder." One brain disorder that involves distortions in visual perception is schizophrenia, but nobody understands the neurobiological basis for those distortions.

"The versatility of the biological model turns computer vision from a trick into something really useful," said co-author Stanley Bileschi, a postdoctoral associate in the Poggio lab.