Fig 5. Effect of evaluation parameters δ_cut and α on the error.

False colours indicate the mean squared difference MSD between the computed angular distributions and their reference on a subset of N = 10³ images for a discrete set of 10 × 10 parameter values. Data points represent the optimal parameter set for each image (N = 10⁴) based on the exploited two parameters Nelder-Mead optimization algorithm which used the squared difference between reference and calculated orientation distribution as objective function. Histograms show the frequency distribution of parameter δ_cut and α. The median values of both distributions(δ_cut = 2.1%, α = 1.5) were identified as optimal evaluation parameters. The mean number of iterations, until the termination criterion was met, is 23 ± 9. Only 0.2% of the images reached the maximum number of iterations, which was set to 100.