A compressed sensing model of peripheral vision

Jens Hocke; Michael Dorr; Erhardt Barth

doi:10.1117/12.908664

A compressed sensing model of peripheral vision

Jens Hocke, Michael Dorr, Erhardt Barth

Institut für Neuro- und Bioinformatik

Abstract

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.

Originalsprache	Englisch
Titel	Human Vision and Electronic Imaging XVII
Redakteure/-innen	Bernice E. Rogowitz, Thrasyvoulos N. Pappas, Huib de Ridder
Seitenumfang	7
Band	8291
Herausgeber (Verlag)	SPIE
Erscheinungsdatum	21.02.2012
Aufsatznummer	82910Z
ISBN (Print)	9780819489388
DOIs	https://doi.org/10.1117/12.908664
Publikationsstatus	Veröffentlicht - 21.02.2012
Veranstaltung	Human Vision and Electronic Imaging 2012 - San Francisco, USA / Vereinigte Staaten Dauer: 23.01.2012 → 26.01.2012 http://users.eecs.northwestern.edu/~pappas/hvei/past.html

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10.1117/12.908664

https://spie.org/Publications/Proceedings/Paper/10.1117/12.908664Lizenz: Nicht angegeben
http://www.inb.uni-luebeck.de/publications/pdfs/HoDoBa12.pdf

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