In fact, it was a funny challenge, since was not easy develop a math model to give results with a good balance between of smoothness and small loss of details.
The result's quality depends on several math properties of original raster image, like for example:
1) average curvature field;
2) topology distribution;
3) local and global entropy.
At first, let's see what kind of result we get by direct use of the vectorization tool over a common bad resolution bitmap:
(1) The original image
(2) The raster representation of same image after vectorization
Beginning from this very simple example, there already are several points to talk about:
1 - the original picture was very easy to vectorize;
2 - the result is not totally smooth;
3 - one intuitive way to see some math properties of source image;
4 - the vectorization was applied just once and without pre-processing.
I will discuss about all of that soon also including new examples.
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