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There's a lot of information on the internet regarding ordinary line simplification,

https://www.jasondavies.com/simplify/

https://bost.ocks.org/mike/simplify/

http://geomalgorithms.com/a16-_decimate-1.html

http://mourner.github.io/simplify-js/

i.e. when the simplified points are known upfront. The Visvalingam’s algorithm, Douglas-Peucker Algorithm, but what if the tolerance parameter is fixed and the points are not known upfront. I have a lot of points and I wouldn't want to run N * Log(N) algorithm M thousand times, instead I'd like it to process my set incrementally, the intersections doesn't matter, the point is just to reduce the size of dataset with minimal visual impact is there some smart way to deal with this problem?

Lu4
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1 Answers1

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If intersections don't matter and all you need is visual similarity (presumably after rasterization and with ε close to the pixel size), simply discarding points that are close enough to the reduced chain may do the job.

In pseudocode:

Let C be the original chain
Let R be the reduced chain (initially empty)

Add the first point of C to R
For every subsequent point p of C:
    If distance(p, the last point of R) >= ε:
        Add p to R

What this approach guarantees:

  • The length of every segment in the reduced chain will be at least ε
  • The Hausdorff distance between chains will be at most ε

What it doesn't guarantee:

  • Absence of self-intersections
  • Any kind of optimality (there may be a different chain that is both closer in Hausdorff distance and smaller in number of points)
Anton
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