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Author Topic: emndl perturbation test | E139  (Read 589 times)
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claude
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« on: June 15, 2013, 07:22:27 AM »



http://archive.org/details/emndl_perturbation_test_2013-06-14

Test render using perturbation method (some of the maths from superfractalthing), with glitches around deep islands and embedded Julia sets - I have some code that tries to detect where the delta method went wrong, but it's not finished, and when it is I'll still need to add more code to re-render the bad parts with more appropriate reference points...  Source code is here in the 'perturbation' branch, but it's fairly hideous/messy at the moment:
https://gitorious.org/maximus/emndl
http://code.mathr.co.uk/emndl
« Last Edit: March 10, 2015, 03:16:19 AM by claude, Reason: gitorious.org is closing » Logged
Kalles Fraktaler
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kallesfraktaler
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« Reply #1 on: June 15, 2013, 10:56:56 AM »

Cool, I like your rendering "lines". It would be really cool if you are able to detect and correct the glitches, I solve this problem by not going too close minibrots on the way to the final minibrot wink
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Dinkydau
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« Reply #2 on: June 15, 2013, 03:30:26 PM »

Nice video
You've got an interesting rendering method, too bad there are those glitches, and I like the music too.
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claude
Fractal Bachius
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« Reply #3 on: June 17, 2013, 05:40:23 PM »

Cool, I like your rendering "lines". It would be really cool if you are able to detect and correct the glitches, I solve this problem by not going too close minibrots on the way to the final minibrot wink

The lines are from using distance estimate for colouring, in HSV space hue = continuous iteration count / 512, saturation = 0.5, value = tanh(distance estimate / pixel size), with non-escaped points coloured separately.

I like going close to lots of minibrots, makes more interesting shapes stack up smiley
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claude
Fractal Bachius
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« Reply #4 on: June 17, 2013, 05:47:53 PM »

Nice video
You've got an interesting rendering method, too bad there are those glitches, and I like the music too.

Thanks, I'll try and fix the glitches later this week - the approach I've been working towards so far is:

  • detect badness by seeing if neighbouring pixels have identical continuous iteration count
  • get rid of tiny regions of badness that might be false positives, and expand larger regions into smooth blobs
  • recalculate each of those blobs using a better reference point
  • repeat until no more badness

http://mathr.co.uk/misc/2013-06-08_detecting_bad_references.png shows the first couple of steps.

The music isn't mine, it's just something I found online that was the right length.
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