  Geometrical illusions are a subclass of optical illusions in which the
geometrical characteristics of patterns such as orientations and angles are
distorted and misperceived as the result of low- to high-level retinal/cortical
processing. Modelling the detection of tilt in these illusions and their
strengths as they are perceived is a challenging task computationally and leads
to development of techniques that match with human performance. In this study,
we present a predictive and quantitative approach for modeling foveal and
peripheral vision in the induced tilt in Caf\'e Wall illusion in which parallel
mortar lines between shifted rows of black and white tiles appear to converge
and diverge. A bioderived filtering model for the responses of retinal/cortical
simple cells to the stimulus using Difference of Gaussians is utilized with an
analytic processing pipeline introduced in our previous studies to quantify the
angle of tilt in the model. Here we have considered visual characteristics of
foveal and peripheral vision in the perceived tilt in the pattern to predict
different degrees of tilt in different areas of the fovea and periphery as the
eye saccades to different parts of the image. The tilt analysis results from
several sampling sizes and aspect ratios, modelling variant foveal views are
used from our previous investigations on the local tilt, and we specifically
investigate in this work, different configurations of the whole pattern
modelling variant Gestalt views across multiple scales in order to provide
confidence intervals around the predicted tilts. The foveal sample sets are
verified and quantified using two different sampling methods. We present here a
precise and quantified comparison contrasting local tilt detection in the
foveal sets with a global average across all of the Caf\'e Wall configurations
tested in this work.
