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20 February 2012 A compressed sensing model of crowding in peripheral vision
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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.
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Jens Hocke, Michael Dorr, and Erhardt Barth "A compressed sensing model of crowding in peripheral vision", Proc. SPIE 8291, Human Vision and Electronic Imaging XVII, 82910Z (20 February 2012); https://doi.org/10.1117/12.908664
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