A Bioplausible Model for Explaining Café Wall Illusion: Foveal vs. Peripheral Resolution

Nasim Nematzadeh, David Powers

    Research output: Contribution to conferencePaper

    2 Citations (Scopus)

    Abstract

    Optical illusions highlight sensitivities and limitations of human visual processing and studying them leads to insights about perception that can potentially help computer vision match or exceed human performance. Geometric illusions are a subclass of illusions in which orientations and angles are distorted and misperceived. In this paper, a quantifiable prediction is presented of the degree of tilt for the Café Wall pattern, a typical geometric illusion, in which the mortar between the tiles seems to converge and diverge. Our study employs a bioplausible model of ON-center retinal processing, using an analytic processing pipeline to measure, quantitatively, the angle of tilt content in the model. The model also predicts different perceived tilts in different areas of the fovea and periphery as the eye saccades to different parts of the image. This variation is verified and quantified in simulations using two different sampling methods. Several sampling sizes and aspect ratios, modeling variant foveal views, are investigated across multiple scales in order to provide confidence intervals around the predicted tilts, and to contrast local tilt detection with a global average across the whole Café Wall image.

    Original languageEnglish
    Pages426-438
    Number of pages13
    DOIs
    Publication statusPublished - 1 Jan 2016
    EventInternational Symposium on Visual Computing ISVC 2016: Advances in Visual Computing -
    Duration: 12 Dec 2016 → …

    Conference

    ConferenceInternational Symposium on Visual Computing ISVC 2016: Advances in Visual Computing
    Period12/12/16 → …

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  • Cite this

    Nematzadeh, N., & Powers, D. (2016). A Bioplausible Model for Explaining Café Wall Illusion: Foveal vs. Peripheral Resolution. 426-438. Paper presented at International Symposium on Visual Computing ISVC 2016: Advances in Visual Computing, . https://doi.org/10.1007/978-3-319-50835-1_39