So, if my understanding from this (and the other comments) is correct, then yeah; kinda. In CV, the first-ish level of processing determines the points on the

So, if my understanding from this (and the other comments) is correct, then yeah; kinda. In CV, the first-ish level of processing determines the points on the image that will be used as "individual measurable properties" (or, maybe more accurately, the sources for those). These are the features, and then they go into the rest of the system for recognition, etc. The basic theme is stripping something analogous to "noise" and "bias" - noise being the stuff you don't want to pay attention to, and "bias" being the predictable/regular offset per thing - because you want to recognize that two things are actually the same thing, despite differences in noise and bias. (An example of "bias" might be the color of the lights where the picture was taken). Then the idea is pretty much that, if the same thing is seen in two different places, after stripping the noise and the bias from it, they "look" identical (diff is within an epsilon). That sound about right?