fractal method for color images

[denfc]

I was looking forward to applying generalized_dim to images, but the consistency difficulties inherent in binarization, e.g., Influence of binarization methods ..., are driving my colleague and I to make our initial forays with a new, indeed "novel" method that promises improved results by using all the information in a color image to find its fractal index, A single-scale fractal feature for classification of color images.

The authors have made their code available in Python, with which I can say at best that I'm familiar with some small fraction of. After not having programmed for a few decades, I've spent the last couple of years learning Julia, which I love, but I am light years away from being expert. Hence, I make this request that you consider porting the code of the Arce, Pierce, Velcsov paper into the Fractal Dimensions package.

I have played with their method (learning about FixedPointNumbers.N0f8 arithmetic), and I did reproduce their amazing result on the Sierpinski carpet, but if in the process of exploring the code, you discover scientific objections to this new method, I would very much like to hear them! Their Python code and the images on which they worked can be found at https://github.com/wsarce/Single-Scale-Fractal-Feature.

If I can assist, please let me know how. Thank you.

-- dfc