by Gooly (Li Yang Ku)
I talked about some visual illusions in my previous post but didn’t mention why they are important to computer vision and the pros of seeing visual illusions. In this post I am gonna talk about the advantage of having two of the most common known visual illusions, Illusory contours and checkerboard illusion.
The Kanizsa’s triangle invented by Gaetano Kanizsa is a very good example of illusory contours. Even though the center upside down triangle doesn’t exist, you are forced to see it because of the clues given by the other parts. If you gradually cover up some of the circles and corners, at some point you would be able to see the pac man and the angle as individual objects and the illusory contours will disappear. This illusion is the side effect of how we perceive objects and shows that we see edges using various clues instead of just light differences. Because our eyes receive noisy real world inputs, illusory contour actually helps us fill in the missing contours caused by lighting, shading, or occlusion. It also explains why a bottom up vision system won’t work in many situations. In the paper “Hierarchical Bayesian inference in the visual cortex” written by Lee and Mumford, a Kanizsa’s square is used to test whether monkeys perceive illusory contours in V1. The result is positive but has a delayed response compared to V2. This suggests that information of illusory contours is possibly generated in V2 and back propagated to V1.
This checkerboard illusion above is done by Edward H. Adelson. In the book “Perception as Bayesian Inference” Adelson wrote a chapter discussing how we perceive objects under different lighting conditions. In other words, how we achieve “lightness constancy”. The illusion above should be easily understandable. At first sight, In the left image square A on the checkerboard seems to be darker than square B although they actually have the same brightness. By breaking the 3D structure, the right images shows that the two squares indeed have the same brightness. We perceive A and B differently in the left image because our vision system is trying to achieve lightness invariant. In fact if the cylinder is removed square A will be darker than square B, therefore lightness constancy actually gives us the correct brightness when only constant lighting is presented. This allows us to recognize the same object even under large lighting changes, which I would argue is an important ability for survival. In the paper “Recovering reflectance and illumination in a world of painted polyhedra” by Sinha and Adelson, how we construct 3D structure from 2D drawing and shading are further discussed. Understanding object’s 3D structure is crucial in obtaining light constancy like the checkerboard illusion above. As in the image below, by removing certain types of junction clues, a 3D drawing can easily be seen flat. However, as mentioned in the paper, more complex global strategies are needed to cover all cases.
I was gonna post this a few month ago but was delayed by my Los Angeles to Boston road trip (and numerous good bye parties), but I am now officially back to school in UMASS Amherst for a PhD program. Not totally settled down yet but enough to make a quick post.