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A quantitative analysis of tilt in the Caf\'e Wall illusion: a bioplausible model for foveal and peripheral vision
Computer Vision and Pattern Recognition · Submitted 22 Sep 2016
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This paper reports quantifiable predictions of the degree of tilt for a typical geometric illusion called Caf\'e Wall, in which the mortar between the tiles seems to tilt or bow.
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The abstract, sentence by sentence
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 Caf\'e 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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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. | background 1.00 | 0.07 | 0.04 | hedged possibility 0.87 |
| s1 | Geometric illusions are a specific subfamily in which orientations and angles are misperceived. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | This paper reports quantifiable predictions of the degree of tilt for a typical geometric illusion called Caf\'e Wall, in which the mortar between the tiles seems to tilt or bow. | contribution 0.96 | 0.84 | 0.22 | asserted 0.99 |
| s3 | 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. | method 0.94 | 0.91 | 0.09 | asserted 1.00 |
| s4 | 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. | contribution 0.65 | 0.86 | 0.07 | asserted 0.93 |
| s5 | 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. | method 0.97 | 0.95 | 0.06 | asserted 0.96 |
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The paper
Original 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 Caf\'e 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.
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