Humans perceive color and identify color with deceptive ease. One workaround is to think of each color variable as an intensity image, but that leads us right back to grayscale image processing. Sure, we can think of "peak redness," but what does that mountain peak look like in an (x,y,h,s,i) space? Ouch. In RGB, HSI, Lab, and other color spaces this sort of visualization is much harder since there are additional dimensions that the standard human brain can't visualize easily. There are a number of algorithms for which an intuitive "physical" interpretation helps us think through a problem. "Peak brightness" is just a mountain peak in our 3D visualization of the grayscale image. In grayscale images, the watershed algorithm is fairly easy to conceptualize because we can think of the two spatial dimensions and one brightness dimension as a 3D image with hills, valleys, catchment basins, ridges, etc.
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