  We propose an algorithm to estimate the Hurst exponent of high-dimensional
fractals, based on a generalized high-dimensional variance around a moving
average low-pass filter. As working examples, we consider rough surfaces
generated by the Random Midpoint Displacement and by the Cholesky-Levinson
Factorization algorithms. The surrogate surfaces have Hurst exponents ranging
from 0.1 to 0.9 with step 0.1, and different sizes. The computational
efficiency and the accuracy of the algorithm are also discussed.
