Artificial vision parameter learning and automating method for improving visual prosthetic systems

Abstract

In artificial vision parameter automating method, the system includes a sensory module, a communication module, a database, an image processing module, a learning module and a automating and storing module. The sensory module is configured to take images. The communication module is configured to be send the data obtained from the sensory module to the database. The database is configured to store the images received from the communication module. The image processing module is configured to convert a selected image from the database into a low-resolution phosphene image, to generate test images with various artificial vision parameters. The learning module is configured to extract important features from high-resolution training images, and to build various machine learning models competent at identifying visual stimuli from low-resolution phosphene images. The automating and storing module is configured to automatically select and store primary test data, based on the learning in the learning module.