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

Ashkan Nejad*, Burcu Küçükoǧlu, Jaap de Ruyter van Steveninck, Sandra Bedrossian, Joost Heutink, Gera A. de Haan, Frans W. Cornelissen, Marcel van Gerven

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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-contingent object discrimination experiment demonstrated that Point-SPV outperformed a conventional edge detection method, by facilitating observers to gain a higher recognition accuracy, faster reaction times, and a more efficient visual exploration. Our work highlights how task-specific optimization may enhance representations in prosthetic vision, offering a foundation for future exploration and application.
Original languageEnglish
Article number1549698
Number of pages12
JournalFrontiers in Human Neuroscience
Volume19
DOIs
Publication statusPublished - 24-Mar-2025

Keywords

  • simulated prosthetic vision
  • synthetic viewing points
  • object recognition
  • end-to-end training
  • deep learning

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