Recognition of a Virtual Maze Scene Using Simulated Prosthetic Vision
A real-time psychological physics experimental platform was built to aid in discovering the best real-time image processing strategy for the limited number of electrodes, and in providing the most useful prosthetic implant visual information. A number of maze pathfinder tasks were performed at low resolution to simulate use by blind individuals in performing daily visual activities. In this study, simple (5 rows × 5 columns), medium (8 × 8), and complex (11 × 11) maze models were created in 3DMAX. The models were constructed by using Unity to build virtual scenes for real-time pixel processing, including binarization, color inversion, and matching to templates with three different resolutions (16 × 16, 24 × 24, 32 × 32). Subjects completed maze pathfinding tasks using 45° and 60° views of the labyrinth at the 32 × 32 resolution to determine the optimal viewing angle. The time required to find the maze entrance and complete the maze were analyzed along with the rate of maze completion (accuracy) at various resolutions. In the first experiment, the average time required to find the entrance and the average maze pathfinding duration were significantly longer at 60°. Therefore, the 45° view provided the best perspective. In the second experiment, the angle was fixed to 45°. As the maze difficulty increased, the average time needed to find the maze entrance decreased, but the average time required to complete the maze increased. When the difficulty of the maze was fixed, the time required to find the maze entrance and solve the maze decreased when the resolution increased. The accuracy with which the maze path was identified increased as well. The average maze pathfinding time at 24 × 24 was significantly less than at 16 × 16. A similar trend was observed when the average maze pathfinding times at 32 × 32 and 24 × 24 were compared. At 32 × 32, the average pathfinding accuracy was 100%. This indicates that 32 × 32 is an effective resolution for maze pathfinding.