NeurIPS 2020poster14 citations
Color Visual Illusions: A Statistics-based Computational Model
Abstract
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.
BibTeX
@inproceedings{NEURIPS2020_6b39183e,
author = {Hirsch, Elad and Tal, Ayellet},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {9447--9458},
publisher = {Curran Associates, Inc.},
title = {Color Visual Illusions: A Statistics-based Computational Model},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/6b39183e7053a0106e4376f4e9c5c74d-Paper.pdf},
volume = {33},
year = {2020}
}