I have found since then, that it is not a 100% viable solution inside of an environment that is cluttered with lots of color. Being in Morocco, I've realized that mainly shops have a very cluttered test environment with lots of different colors. So the clutter allows for some level of failure based on the color bias of any given perceptoid to still get stuck on points which the user/programmer deems "not important".
While in an uncluttered environment, color based perceptoids perform extremely accurately, the cluttered environment tends to pose the same exact problem that caused me to begin experimenting with point color to begin with. The problem exists in fewer situations, but it is still there.
I am now working toward adding in short-term session based memory and shape recognition into each perceptoid so that this recognition can help the perceptoid to predict movement, orientation, and which colors will be included.
I think to fully represent colors I'm going to have to experiment with more advanced color theory like frequencies, RGB race conditions, hue decomposition, etc.
I'll keep you all posted ;)
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