I have an idea that it might be approached by processing a closed path off-line.
Here are a couple of figures: of the original, simple path [Edit: created with Path tool], and of the enclosing path created with Save-Selection-As-Path [Edit: after stroking the first path with a 1-pixel brush and selecting that].
Attachment:
simple-open-path.png [ 46.11 KiB | Viewed 3812 times ]
Attachment:
compound-closed-path.png [ 55.82 KiB | Viewed 3812 times ]
From staring at these and my real problem, it occurred to me--and probably laughably obvious to everyone else--that nodes "close" to each other should be replaced by their average to simplify the representation.
A websearch on data clustering turned up something called the Markov Cluster Algorithm, MCL, which promises to simplify
"networks" of nodes and weighted, as I understand it, edges.
http://www.micans.org/mcl/Now,
if a closed-path representation could be read out into a network representation, it might be fed to a MCL to be "reduced/relaxed" to a simpler network, which could be read back out as, at least, a simpler closed-path representation.
Failing that, the nodes could be read out of the XML representation of the exported path and classified onto lists for later evaluation, processing and re-classification.
Nutty. I know. But my other idea was to try to "walk" a node between the contours of a closed-path, zapping it if it crossed either--like they were part of an electric fence. In fact, that reminded me of a phrase I know very little about: random walk. And hunting up some background information on that to add to this pile of motivation, I discovered that "[an] image is modeled as a graph, in which each pixel corresponds to a node which is connected to neighboring pixels by edges, and the edges are weighted to reflect the similarity between the pixels."[1]
Apparently all "we" need to do is to have "you" [2] program the random-walk algorithm for an image of the closed path on a background...
[1]
https://en.wikipedia.org/wiki/Random_walker_algorithm[2] Theoretically, "I" could learn from here, given enough time[3]:
https://www.khanacademy.org/computing/c ... ndom-walks[3]
https://en.wikipedia.org/wiki/Infinite_monkey_theorem