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Otsu's method

Computing the optimal global threshold automatically by maximizing the separation between two intensity classes.

Otsu's method picks the global threshold T that maximizes the between-class variance \sigma_B^2(T) = w_0(T)w_1(T)\big(\mu_0(T)-\mu_1(T)\big)^2, where w_0,w_1 are the two classes' own population fractions and \mu_0,\mu_1 their own mean intensities, evaluated over every candidate split of the image's own histogram. It removes the guesswork from choosing T by hand, but is still a single GLOBAL value -- it inherits every one of global thresholding's own uneven-lighting weaknesses.

See also

global threshold adaptive (local) thresholding
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