Color Visual Illusions: A Statistics-based Computational Model
This work addresses a fundamental challenge in neuroscience and computer vision by providing a unified, data-driven approach to understanding visual illusions, though it appears incremental in building on existing input-driven paradigms.
The authors tackled the problem of explaining color and lightness visual illusions by developing a computational model that uses a new tool to compute patch likelihoods from large datasets, enabling both explanation and generation of illusions in natural images.
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.