The program transforms an input image using the differential box counting algorithm to a fractal dimension (FD) image, i.e. each pixel has its own FD. Then the user can select any region of interest in the generated FD image to estimate the corresponding mean, standard deviation and lacunarity.
nicely done ..thanks a lot...
@Rajkumar Why to use this for signal processing? There are algorithms based on kNN, Higuchi's method and Multi-resolution box count that can perform much better for time series data.
can this algorithm be used for signal processing? if yes then how?
Thanks Martin, and I'm happy you found the script useful.
Yes as you guessed, the standard deviation mainly depends on the type of image you anslyse. That is, the more homogeneous the texture in the image is, the more homogeneous the Fractal dimension becomes, and thus the lower the standard deviation; and vice versa. To check, try to apply the script to images with different textures (e.g. rough and fine) and compare your results.
Thanks for useful script.
But the estimate of fractal dimension has large standard deviation in many case when I used this script. Is it a proprerty of box-method or it depends on source image?
Thank for respond.
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