Computational neuroscientists at Carnegie Mellon University have developed a computational model that provides insight into the function of the brain's visual cortex and the information processing that enables people to perceive contours and surfaces, and understand what they see in the world around them.
A type of visual neuron known as simple cells can detect lines, or edges, but the computation they perform is insufficient to make sense of natural scenes, said Michael S. Lewicki, associate professor in Carnegie Mellon's Computer Science Department and the Center for the Neural Basis of Cognition. Edges often are obscured by variations in the foreground and background surfaces within the scene, he said, so more sophisticated processing is necessary to understand the complete picture. But little is known about how the visual system accomplishes this feat.
In a paper published online by the journal Nature , Lewicki and his graduate student, Yan Karklin, outline their computational model of this visual processing. The model employs an algorithm that analyzes the myriad patterns that compose natural scenes and statistically characterizes those patterns to determine which patterns are most likely associated with each other.
The bark of a tree, for instance, is composed of a multitude of different local image patterns, but the computational model can determine that all these local images represent bark and are all part of the same tree, as well as determining that those same patches are not part of a bush in the foreground or the hill behind it.
"Our model takes a statistical approach to making these generalizations about each patch in the image," said Lewicki, who currently is on sabbatical at the Institute for Advanced Study in Berlin. "We don't know if the visual system computes exactly in this way, but it is behaving as if it is."