This week, we’ll discuss a cool, but failed, experiment.
In the last few weeks (of posts, because in real time, I worked on that problem over a week-end) we’ve looked at how to generate well distributed, maximally different colors. The methods were to use well-distributed sequences or lattices to ensure that the colors are equidistant. What if we used physical analogies to push the colors around so that they are maximally apart?
Last week, we’ve had a look at how to distribute maximally different colors on the RGB cube. But I also remarked that we could use some other color space, say HSV. How do we distribute colors uniformly in HSV space?
When we use false color to encode useful information in an image, it helps greatly if the colors are meaningful in themselves (like a rainbow to encode heat) or maximally different when the image is segmented (like a map showing geologic provinces). But how do we chose those maximally different colors?
Somehow, we need a maximally distributed set of points in RGB space (but not necessarily RGB). We might have just what we need for this! We’ve discussed Halton sequences before. They’re a simple way of progressively and uniformly distribute points over an interval. The sequence starts by the ends of the interval then progressively fills the gaps. It generates the sequence 0, 1, 0.5, 0.25, 0.75, 0.125, 0.625, 0.375, 0.875, …