Point-SPV: end-to-end enhancement of object recognition in simulated prosthetic vision using synthetic viewing points.

ABSTRACT: Prosthetic vision systems aim to restore functional sight for visually impaired individuals by replicating visual perception by inducing phosphenes through electrical stimulation in the visual cortex, yet there remain challenges in visual representation strategies such as including gaze information and task-dependent optimization. In this paper, we introduce Point-SPV, an end-to-end deep learning model designed to enhance object recognition in simulated prosthetic vision. Point-SPV takes an initial step toward gaze-based optimization by simulating viewing points, representing potential gaze locations, and training the model on patches surrounding these points. Our approach prioritizes task-oriented representation, aligning visual outputs with object recognition needs. A behavioral gaze-con

SUBMITTER: Nejad A 

PROVIDER: S-EPMC11973266 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature