Alternative approaches to optophonic mappings

  • Michael Capp

Student thesis: Doctoral Thesis


This thesis presents a number of modifications to a blind aid, known as the video optophone, which enables a blind user to more readily interpret their local environment for enhanced mobility and navigation. Versions of this form of blind aid are generally both difficult to use and interpret, and are therefore inadequate for safe mobility. The reason for this severe problem lies in the complexity and excessive bandwidth of the optophonic output after the conversion from scene-to-sound. The work herein describes a number of modifications that can be applied to the current optophonic process to make more efficient use of the limited bandwidth provided by the auditory system when converting scene images to sound. Various image processing and stereo techniques have been employed to artificially emulate the human visual system through the use of depth maps that successfully fade out the quantity of relatively unimportant image features, whilst emphasising the more significant regions such as nearby obstacles. A series of experiments were designed to test these various modifications to the optophonic mapping by studying important factors of mobility and subject response whilst going about everyday life. The devised system, labelled DeLIA for the Detection, Location, Identification, and Avoidance (or Action) of obstacles, provided a means for gathering statistical data on users’ interpretation of the optophonic output. An analysis of this data demonstrated a significant improvement when using the stereo cartooning technique, developed as part of this work, over the more conventional plain image as an input to an optophonic mapping from scene-to-sound. Lastly, conclusions were drawn from the results, which indicated that the use of a stereo depth map as an input to a video optophone would improve its usefulness as an aid to general mobility. For the purposes of detecting and determining text or similar detail, either a plain unmodified image or some form of edge (depth) image were found to produce the best results
Date of Award2000
Original languageEnglish
Awarding Institution
  • University of Northampton
SupervisorPhilip Picton (Supervisor)

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