Moving object recognition under simulated prosthetic vision using background-subtraction-based image processing strategies

A visual prosthesis that applies electrical stimulation to different parts of the visual pathway has been proposed as a viable approach to restore functional vision. However, the created percept is currently limited due to the low-resolution images elicited from a limited number of stimulating electrodes. Thus, methods to optimize the visual percepts providing useful visual information are being considered. We used two image-processing strategies based on a novel background subtraction technique to optimize the content of dynamic scenes of daily life. Psychophysical results showed that background reduction, or background reduction with foreground enhancement, increased response accuracy compared with methods that directly merged pixels to lower resolution. By adding more gray scale information, a background reduction/foreground enhancement strategy resulted in the best performance and highest recognition accuracy. Further development of image-processing modules for a visual prosthesis based on these results will assist implant recipients to avoid dangerous situations and attain independent mobility in daily life.