  Prosthetic vision based on phosphenes is a promising way to provide visual
perception to some blind people. However, phosphenic images are very limited in
terms of spatial resolution (e.g.: 32 x 32 phosphene array) and luminance
levels (e.g.: 8 gray levels), which results in the subject receiving very
limited information about the scene. This requires using high-level processing
to extract more information from the scene and present it to the subject with
the phosphenes limitations. In this work, we study the recognition of indoor
environments under simulated prosthetic vision. Most research in simulated
prosthetic vision is performed based on static images, while very few
researchers have addressed the problem of scene recognition through video
sequences. We propose a new approach to build a schematic representation of
indoor environments for phosphene images. Our schematic representation relies
on two parallel CNNs for the extraction of structural informative edges of the
room and the relevant object silhouettes based on mask segmentation. We have
performed a study with twelve normally sighted subjects to evaluate how our
methods were able to the room recognition by presenting phosphenic images and
videos. We show how our method is able to increase the recognition ability of
the user from 75% using alternative methods to 90% using our approach.
