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• W1999332595 startingPage "15" @default.
• W1999332595 abstract "Letter identification is an important visual task for both practical and theoretical reasons. To extend and test existing models, we have reviewed published data for contrast sensitivity for letter identification as a function of size and have also collected new data. Contrast sensitivity increases rapidly from the acuity limit but slows and asymptotes at a symbol size of about 1 degree. We recast these data in terms of contrast difference energy: the average of the squared distances between the letter images and the average letter image. In terms of sensitivity to contrast difference energy, and thus visual efficiency, there is a peak around ¼ degree, followed by a marked decline at larger sizes. These results are explained by a Neural Image Classifier model that includes optical filtering and retinal neural filtering, sampling, and noise, followed by an optimal classifier. As letters are enlarged, sensitivity declines because of the increasing size and spacing of the midget retinal ganglion cell receptive fields in the periphery." @default.
• W1999332595 created "2016-06-24" @default.
• W1999332595 creator A5003649165 @default.
• W1999332595 creator A5065568714 @default.
• W1999332595 date "2015-02-12" @default.
• W1999332595 modified "2023-09-25" @default.
• W1999332595 title "Letter identification and the Neural Image Classifier" @default.
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• W1999332595 doi "https://doi.org/10.1167/15.2.15" @default.
• W1999332595 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25761333" @default.
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