We here model peripheral vision in a compressed sensing framework as a strategy of optimally guessing what stimulus corresponds to a sparsely encoded peripheral representation, and find that typical letter-crowding effects naturally arise from this strategy. The model is simple as it consists of only two convergence stages. We apply the model to the problem of crowding effects in reading. First, we show a few instructive examples of letter images that were reconstructed from encodings with different convergence rates. Then, we present an initial analysis of how the choice of model parameters affects the distortion of isolated and flanked letters.

Date Published: 20 February 2012
PDF: 7 pages
Proc. SPIE 8291, Human Vision and Electronic Imaging XVII, 82910Z (20 February 2012); doi: 10.1117/12.908664

Published in SPIE Proceedings Vol. 8291:
Human Vision and Electronic Imaging XVII
Bernice E. Rogowitz; Thrasyvoulos N. Pappas; Huib de Ridder, Editor(s)