This plugin offers image segmentation algorithms using a global approach based on the histogram. Depending on the application and the type of image, several criteria are available to detect many objects of interest [Otsu, 1979] [Diday, 1971] [Dempster, 1987] or few objects [Kapur, 1985] [ Huang, 1995]. A strategy to automatically determine the number of classes is also proposed [Yin, 1997].
More details can be found in a course (in French) given to students of CPE Lyon (see link below).

Update 20190109 : Added manual segmentation method and help.

References:
[Otsu, 1979] N. Otsu. A threshold selection method from gray-level histograms , IEEE Trans. Sys., Man., Cyber., vol. 9,‎ p. 62–66 (1979).
[Diday, 1971] E. Diday. Une nouvelle méthode en classification automatique et reconnaissance des formes. La méthode des nuées dynamiques, Revue de Statistiques Appliquées, vol. 20, 2, p. 19-33 (1971).
[Dempster, 1977] A. Dempster, N. Laird et D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), p. 1–38 (1977).
[Kapur, 1985] J. Kapur, P. Sahoo, A. Wong. A new method for gray-level picture thresholding using the entropy of the histogram. Computer vision, graphics, and image processing, 29(3), p. 273–285 (1985).
[Huang, 1995] L.-K. Huang et M. Wang. Image thresholding by minimizing the measures of fuzziness. Pattern Recognition, 28(1), p. 41–51 (1995).
[Yin, 1997] P. Yin, L. Chen A fast iterative scheme for multilevel thresholding methods, Signal Processing A 60 3, p. 305-13 (1997).

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