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I've recently saw some examples of what deep neural networks can do. I wonder if they could be used to "intelligently" trace bitmaps creating vector shapes form low-res images?
Possibly preporcessing the image with a sobel & canny edge detector would be useful.
Probably such a neural network could "see" a fixed part of the input image (a circle of for example 8 pixels radius) and travel along the shape's edge, creating/refining bezier curve nodes and control points as it goes around.
Possibly useful resources:
https://www.youtube.com/watch?v=S9Y_I9vY8Qw - a neural network learning to play a racing game
https://www.youtube.com/watch?v=uihBwtPIBxM - finding edges in a bitmap: Sobel
https://www.youtube.com/watch?v=sRFM5IEqR2w - finding edges in a bitmap: Canny edge Detector
http://machinelearningmastery.com/tutor ... hon-keras/ - tutorial in creating a simple neural network in Python using Keras library
I guess training /evaluation data could consist of pairs of svg files of different black shapes on white background, and bitmap exports of these shapes.
The net would try to create a bezier path as close to the original one, and then a high resolution rendering of both (approximated and original beziers) can be pixel compared to define the score. Each different pixel increases the loss.
Example:
We draw 1000 random convex black shapes on white background in a 256 x 256 canvas.
All of these get rendered 1:1 and converted to grayscale PNG.
The neural net tries to approximate the beziers based on that low-res bitmaps.
Then we render the neural-net created beziers with 4x the resolution (1024x1024) and compare that to the 1024x1024 rendering of the original shapes.
Each pixel that isn't identical on both images increases the loss, so we can asses how well it did.
Tadam!
There are some holes to fill in here, but this at least can be fun. At most - it could become really useful one day.
