A Clearer Picture of Vision

The human retina — the part of the eye that converts incoming light into electrochemical signals — has about 100 million light-sensitive cells. So retinal images contain a huge amount of data. High-level visual-processing tasks — like object recognition, gauging size and distance, or calculating the trajectory of a moving object — couldn’t possibly preserve all that data: The brain just doesn’t have enough neurons. So vision scientists have long assumed that the brain must somehow summarize the content of retinal images, reducing their informational load before passing them on to higher-order processes. At the Society of Photo-Optical Instrumentation Engineers’ Human Vision and Electronic Imaging conference research scientists from the Department of Brain and Cognitive Sciences, presented a new mathematical model of how the brain does that summarizing. The model accurately predicts the visual system’s failure on certain types of image-processing tasks, a good indication that it captures some aspect of human cognition.

Most models of human object recognition assume that the first thing the brain does with a retinal image is identify edges — boundaries between regions with different light-reflective properties — and sort them according to alignment: horizontal, vertical and diagonal. Then, the story goes, the brain starts assembling these features into primitive shapes, registering, for instance, that in some part of the visual field, a horizontal feature appears above a vertical feature, or two diagonals cross each other. From these primitive shapes, it builds up more complex shapes — four L’s with different orientations, for instance, would make a square — and so on, until it’s constructed shapes that it can identify as features of known objects. While this might be a good model of what happens at the center of the visual field, researchers argue that it’s probably less applicable to the periphery, where human object discrimination is notoriously weak.

More information:

http://web.mit.edu/newsoffice/2011/vision-coding-0128.html