December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Efficient Dataflow Modeling of Peripheral Encoding
Author Affiliations
  • Rachel Brown
    NVIDIA
  • Vasha Dutell
    NVIDIA
    UC Berkeley
  • Bruce Walter
    Cornell University
  • Ruth Rosenholtz
    Massachusetts Institute of Technology
  • Peter Shirley
    NVIDIA
  • Morgan McGuire
    NVIDIA
  • David Luebke
    NVIDIA
Journal of Vision December 2022, Vol.22, 4378. doi:https://doi.org/10.1167/jov.22.14.4378
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      Rachel Brown, Vasha Dutell, Bruce Walter, Ruth Rosenholtz, Peter Shirley, Morgan McGuire, David Luebke; Efficient Dataflow Modeling of Peripheral Encoding. Journal of Vision 2022;22(14):4378. https://doi.org/10.1167/jov.22.14.4378.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Identifying a visual stimulus in the periphery can be substantially impaired by the presence of other, nearby stimuli -- a phenomenon called “crowding”. Existing crowding models require significant compute time and their structure does not support easy modification, making it difficult to conduct large scale perceptual validation experiments. We advance crowding models of peripheral vision using a more efficient dataflow computational strategy which also simplifies adding and removing features. We leverage this flexibility by adding Gaussian filters that reduce ringing and encoding “end stopped” features which were missing from previous models. We evaluate our model in the context of texture recognition and letter crowding tasks in the periphery. Our improved computational framework may simplify development and testing of more sophisticated, complete models in more robust and realistic settings relevant to human vision and computer graphics.

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