Optical Illusions Images Dataset
This work provides a dataset for computer vision researchers to study optical illusions, but it is incremental as it focuses on data collection without major breakthroughs.
The authors tackled the problem of understanding human visual perception by creating a dataset of 6725 optical illusion images from websites and 500 hand-picked images, with the result being the presentation of this dataset and initial models trained on it to aid computer vision research.
Human vision is capable of performing many tasks not optimized for in its long evolution. Reading text and identifying artificial objects such as road signs are both tasks that mammalian brains never encountered in the wild but are very easy for us to perform. However, humans have discovered many very specific tricks that cause us to misjudge color, size, alignment and movement of what we are looking at. A better understanding of these phenomenon could reveal insights into how human perception achieves these feats. In this paper we present a dataset of 6725 illusion images gathered from two websites, and a smaller dataset of 500 hand-picked images. We will discuss the process of collecting this data, models trained on it, and the work that needs to be done to make it of value to computer vision researchers.