论文标题
颜色视觉错觉：基于统计的计算模型
Color Visual Illusions: A Statistics-based Computational Model
论文作者
论文摘要
正如神经科学中输入驱动的范式所论证的那样，可以通过现实世界中图像中的斑块的可能性来解释视觉错觉。但是，过去的数据和工具都不存在以广泛支持这些解释。大数据时代为研究输入驱动的方法开辟了新的机会。我们介绍了一个计算补丁可能性的工具，并给出了一个大型数据集可以学习的工具。鉴于此工具，我们提出了一个支持该方法的模型，并以统一的方式解释了轻度和颜色的视觉错觉。此外，我们的模型通过使用相同的工具反向使用自然图像中的视觉错觉。
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.
