Seeing and Perceiving

Two paths to color constancy Between our last post and the exercises in the Color Constancy lab, I hope you have a sense that your perception of color is definitely not just based on the wavelengths that are present in a mixture of lights. Instead, your visual system is doing something to adjust those wavelengths to give you a different estimate of color. But what is that estimate really for and how does your visual system arrive at it? Here, we’re going to start by trying to formalize the problem within a perceptual constancy   framework. This will involve describing a particular goal for the visual system and then pointing out why this goal is particularly challenging. Next, we’ll discuss two different ways to try and achieve this goal that rest on some specific assumptions about the way images of real world objects might work. We’ll see that these assumptions aren’t always true, but they’re true enough of the time that these procedures work. To describe what the visual sy...

Color Constancy: Estimating object and surface color from the data. In our last post, we introduced a new kind of computation that we said was supposed to help us achieve something called perceptual constancy . That term referred to the ability to maintain some kind of constant response despite a pattern of light that was changing. For example, complex cells in V1 might be able to continue responding the same way to a line or edge that was at different positions in the visual field. This would mean that even when an object changed position over time because you or the object were moving, your complex cells might be able to keep doing the same thing throughout that movement. This is a useful thing to be able to do because your visual world changes a lot as time passes, but in terms of the real objects and surfaces that you’re looking at, the world is pretty stable. Think about it: If you just move your eyes around the room you’re sitting in, your eyes will get very different pattern...

Logic gates, complex cells and perceptual constancy In the last post, we demonstrated how we could use the information from a group of V1 cells (a population code ) to make a good guess about the orientation of a line or edge that those cells were responding to. This allowed us to solve some problems related to how to measure values for some image feature (in this case, orientation), using a relatively small number of sensors. The use of a population code allowed us to measure orientation fairly precisely even if we only had a few cells that had a few different preferred orientations, and it also allowed us to make predictions about what would happen if some of those cells changed the way they were responding to a pattern due to adaptation . At this point, I’d say we have a decent grasp of how to measure some simple spatial features in images: We know how to encode wavelength information with photoreceptors, we know how to measure local increments and decrements of light with cells ...