Color Visual Illusions: A Statistics-based Computational Model

Visual illusions may be explained by the likelihood of patches in real-world images, as argued by input-driven paradigms in Neuro-Science. However, neither the data nor the tools existed in the past to extensively support these explanations. The era of big data opens a new opportunity to study input-driven approaches. We introduce a tool that computes the likelihood of patches, given a large dataset to learn from. Given this tool, we present a model that supports the approach and explains lightness and color visual illusions in a unified manner. Furthermore, our model generates visual illusions in natural images, by applying the same tool, reversely. synthetic [16] manipulated leaf synthetic [3] manipulated carpet (a) Simultaneous-Contrast Illusion (b) White's Illusion Figure 1: Examples of visual illusions. (a) Look at the central rectangles. Do they have the same intensity/color? It seems they do not, but they actually do. (b) The same effect happens for the gray long rectangles-they are perceived as having different intensities, although their intensities are equal. This paper provides a general, data-driven explanation to these (and other) illusions. It also presents a model that generates illusions by modifying given natural images in accordance with the above explanation (the right illusions in (a) & (b)). Please watch the illusions on a computer screen.