BOXCOUNT Box-Counting of a D-dimensional array (with D=1,2,3).
The Box-counting method is useful to determine fractal properties of a
1D segment, a 2D image or a 3D array. If C is a fractal set, with fractal dimension DF < D, then the number N of boxes of size R needed to cover the set scales as R^(-DF). DF is known as the Minkowski-Bouligand dimension, or Kolmogorov capacity, or Kolmogorov dimension, or simply box-counting dimension.
[N, R] = BOXCOUNT(C), where C is a D-dimensional array (with D=1,2,3), counts the number N of D-dimensional boxes of size R needed to cover the nonzero elements of C. The box sizes are powers of two, i.e., R = 1, 2, 4 ... 2^P, where P is the smallest integer such that MAX(SIZE(C)) <= 2^P. If the sizes of C over each dimension are smaller than 2^P, C is padded with zeros to size 2^P over each dimension (e.g., a 320-by-200 image is padded to 512-by-512). The output vectors N and R are of size P+1. For a RGB color image (m-by-n-by-3 array), a summation over the 3 RGB planes is done first.
BOXCOUNT(C,'plot') also shows the log-log plot of N as a function of R
(if no output argument, this option is selected by default).
BOXCOUNT(C,'slope') also shows the semi-log plot of the local slope DF = - dlnN/dlnR as a function of R. If DF is contant in a certain range of R, then DF is the fractal dimension of the set C.
The execution time depends on the sizes of C. It is fastest for powers
of two over each dimension.
c = (rand(1,2048)<0.2);
c = randcantor(0.8, 512, 2);
figure, boxcount(c, 'slope');
Frederic Moisy (2021). boxcount (https://www.mathworks.com/matlabcentral/fileexchange/13063-boxcount), MATLAB Central File Exchange. Retrieved .
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