A compressed sensing model of peripheral vision

Jens Hocke, Michael Dorr, Erhardt Barth


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.

Original languageEnglish
Title of host publicationHuman Vision and Electronic Imaging XVII
EditorsBernice E. Rogowitz, Thrasyvoulos N. Pappas, Huib de Ridder
Number of pages7
Publication date21.02.2012
Article number82910Z
ISBN (Print)9780819489388
Publication statusPublished - 21.02.2012
EventHuman Vision and Electronic Imaging 2012 - San Francisco, United States
Duration: 23.01.201226.01.2012


Dive into the research topics of 'A compressed sensing model of peripheral vision'. Together they form a unique fingerprint.

Cite this