A Quantitative Analysis of Tilt in the Café Wall Illusion: A Bioplausible Model for Foveal and Peripheral Vision

Nasim Nematzadeh, David Powers

    Research output: Contribution to conferencePaper

    4 Citations (Scopus)

    Abstract

    The biological characteristics of human visual processing can be investigated through the study of optical illusions and their perception, giving rise to intuitions that may improve computer vision to match human performance. Geometric illusions are a specific subfamily in which orientations and angles are misperceived. This paper reports quantifiable predictions of the degree of tilt for a typical geometric illusion called Café Wall, in which the mortar between the tiles seems to tilt or bow. Our study employs a common bioplausible model of retinal processing and we further develop an analytic processing pipeline to quantify and thus predict the specific angle of tilt. We further study the effect of resolution and feature size in order to predict the different perceived tilts in different areas of the fovea and periphery, where resolution varies as the eye saccades to different parts of the image. In the experiments, several different minimal portions of the pattern, modeling monocular and binocular foveal views, are investigated across multiple scales, in order to quantify tilts with confidence intervals and explore the difference between local and global tilt.

    Original languageEnglish
    DOIs
    Publication statusPublished - 22 Dec 2016
    EventDigital Image Computing: Techniques and Applications (DICTA), 2016 -
    Duration: 30 Nov 2016 → …

    Conference

    ConferenceDigital Image Computing: Techniques and Applications (DICTA), 2016
    Period30/11/16 → …

    Keywords

    • Bioinspired neural networks; Geometric illusions
    • Café Wall illusion
    • Difference of Gaussian
    • Pattern recognition
    • Tilt effects
    • Visual perception

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  • Cite this

    Nematzadeh, N., & Powers, D. (2016). A Quantitative Analysis of Tilt in the Café Wall Illusion: A Bioplausible Model for Foveal and Peripheral Vision. Paper presented at Digital Image Computing: Techniques and Applications (DICTA), 2016, . https://doi.org/10.1109/DICTA.2016.7796995