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One of the biggest problems for blind people is to recognize environments. Prosthetic Vision is a promising new technology to provide visual perception to people with some kind of blindness, transforming an image to a phosphenes pattern to be sent to the implant. However, current prosthetic implants have limited ability to generate images with detail required for understanding an environment. Computer vision play a key role in providing prosthetic vision to alleviate key restrictions of blindness. In this work, we propose a new approach to build a schematic representation of indoor environments for phosphene images. We combine computer vision and deep learning techniques to extract structural features in a scene and recognize different indoor environments designed to prosthetic vision. Our method uses the extraction of structural informative edges which can underpin many computer vision tasks such as recognition and scene understanding, being key for conveying the scene structure. We also apply an object detection algorithm by using an accurate machine learning model capable of localizing and identifying multiple objects in a single image. Further, we represent the extracted information using a phosphenes pattern. The effectiveness of this approach is tested with real data from indoor environments with eleven volunteers.

Indoor Scenes Understanding for Visual Prosthesis with Fully Convolutional Networks