There are some sights and noises that people just can't help but notice. Indeed, research in neuroscience now suggests that the recognition of salient objects is a key part of how we make sense of our environment. But building robots that can intelligently pick out items of interest using sight or sound remains a daunting challenge. So a handful of engineers are working on a new approach called selective-attention modeling, which attempts to program robots to evaluate scenes critically as some neuroscientists believe people do.

"General scene understanding is the Holy Grail for computer vision," says University of Southern California computer scientist Laurent Itti. Neuroscience-based algorithms, he contends, "should be the new approach."

Several research groups, including ones at Caltech and Itti's lab at the USC, are giving robots pan-and-tilt cameras for eyes and the ability to pick out unusual objects. The robots are designed to notice, for example, a bright purple tree house at the side of a wooded road and to do it for the same reason people would-because it stands out against a backdrop of trees. Responding to eye-catching sights enables robots to act independently when they encounter something unexpected. A robot on Mars, for example, might notice an area of the ground that is discolored and take a sample of it. In contrast, robots using more traditional vision methods would detect the discolored ground only if they had been told specifically to look for it.

Itti's robots construct maps on the basis of such local contrasts in features as color, edges, orientation, light intensity and motion. The tree house would stand out on the orientation map because of its horizontal profile amidst the vertical trees. It would also be conspicuous on the color map. The robot would overlay these maps to build a composite that highlights any striking areas.