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.
|Number of pages
|Published - 1 Jan 2016
|International Symposium on Visual Computing ISVC 2016: Advances in Visual Computing -
Duration: 12 Dec 2016 → …
|International Symposium on Visual Computing ISVC 2016: Advances in Visual Computing
|12/12/16 → …