Configuration‐Based Processing of Phosphene Pattern Recognition for Simulated Prosthetic Vision

Visual prosthesis can elicit phosphenes by stimulating the retina, optic nerve, or visual cortex along the visual pathway. Psychophysical studies have demonstrated that visual function can be partly recovered with phosphene‐based prosthetic vision. This study investigated the cognitive process of prosthetic vision through a face recognition task. Both behavioral response and the face‐specific N170 component of event‐related potential were analyzed in the presence of face and non‐face stimuli with natural and simulated prosthetic vision. Our results showed that: (i) the accuracy of phosphene face recognition was comparable with that of the normal one when phosphene grid increased to 25 × 21 or more; (ii) shorter response time was needed for phosphene face recognition; and (iii) the N170 component was delayed and enhanced under phosphene stimuli. It was suggested that recognition of phosphene patterns employ a configuration‐based holistic processing mechanism with a distinct substage unspecific to faces.     

Image Processing Strategies Based on a Visual Saliency Model for Object Recognition Under Simulated Prosthetic Vision

Retinal prostheses have the potential to restore partial vision. Object recognition in scenes of daily life is one of the essential tasks for implant wearers. Still limited by the low‐resolution visual percepts provided by retinal prostheses, it is important to investigate and apply image processing methods to convey more useful visual information to the wearers. We proposed two image processing strategies based on Itti's visual saliency map, region of interest (ROI) extraction, and image segmentation. Itti's saliency model generated a saliency map from the original image, in which salient regions were grouped into ROI by the fuzzy c‐means clustering. Then Grabcut generated a proto‐object from the ROI labeled image which was recombined with background and enhanced in two ways—8‐4 separated pixelization (8‐4 SP) and background edge extraction (BEE). Results showed that both 8‐4 SP and BEE had significantly higher recognition accuracy in comparison with direct pixelization (DP). Each saliency‐based image processing strategy was subject to the performance of image segmentation. Under good and perfect segmentation conditions, BEE and 8‐4 SP obtained noticeably higher recognition accuracy than DP, and under bad segmentation condition, only BEE boosted the performance. The application of saliency‐based image processing strategies was verified to be beneficial to object recognition in daily scenes under simulated prosthetic vision. They are hoped to help the development of the image processing module for future retinal prostheses, and thus provide more benefit for the patients.     

Object Recognition Under Distorted Prosthetic Vision

Psychophysical studies have reported the efficacy of phosphene‐based prosthetic vision in partly recovering the visual function of blind individuals. However, results by far have been based on evenly aligned phosphene arrays, which neglected the complicated visuotopy in the visual prosthesis system. In this study, we investigated how the objects were recognized under the stimuli with distorted phosphene arrays simulated by transformations of barrel distortion, rotation, or translation. The results revealed that distortions significantly decreased the accuracy of categorization (CA) and showed distinct interactive effects with the factors of object category and phosphene array density. Moreover, the CA changed differently with the increase of distortion levels. Regression analysis suggested a phosphene array of at least 10 × 10 be the essential for achieving a CA over the threshold value (CA_t = 62.5%) under distorted prosthetic vision. It is recommended that discriminative features be extracted to improve the performance of prosthetic vision.     

Recognition of Objects in Simulated Irregular Phosphene Maps for an Epiretinal Prosthesis

Visual prostheses offer a possibility of restoring vision to the blind. It is necessary to determine minimum requirements for daily visual tasks. To investigate the recognition of common objects in daily life based on the simulated irregular phosphene maps, the effect of four parameters (resolution, distortion, dropout percentage, and gray scale) on object recognition was investigated. The results showed that object recognition accuracy significantly increased with an increase of resolution. Distortion and dropout percentage had significant impact on the object recognition; with the increase of distortion level and dropout percentage, the recognition decreased considerably. The accuracy decreased significantly only at gray level 2, whereas the other three gray levels showed no obvious difference. The two image processing methods (merging pixels to lower the resolution and edge extraction before lowering resolution) showed significant difference on the object recognition when there was a high degree of distortion level or dot dropout.