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While in some cases, the threshold <math>T</math> can or should be selected manually by the user, there are many cases where the user wants the threshold to be automatically set by an algorithm. In those cases, the threshold should be the "best" threshold in the sense that it should separate in two classes the brighter objects considered to be part of the foreground and the darker objects considered to be part of the background.
Many types of automatic thresholding methods exist, the most famous and widely used being [[Otsu's method]]. The following list, based on the works of [[#Sezgin2004|Sezgin et al. (2004)]] categorizes thresholding methods into broad groups based on the information the algorithm manipulates. Note however that such a categorization is necessarily fuzzy as some methods can fall in several categories (for example, Otsu's method can be both considered a histogram-shape and a clustering algorithm)
* '''[[Histogram]] shape'''-based methods, where, for example, the peaks, valleys and curvatures of the smoothed histogram are analyzed.<ref>{{Cite journal |last=Zack |first=G W |last2=Rogers |first2=W E |last3=Latt |first3=S A |date=July 1977 |title=Automatic measurement of sister chromatid exchange frequency. |url=http://journals.sagepub.com/doi/10.1177/25.7.70454 |journal=Journal of Histochemistry & Cytochemistry |language=en |volume=25 |issue=7 |pages=741–753 |doi=10.1177/25.7.70454 |issn=0022-1554}}</ref> Note that these methods, more than others, make certain assumptions about the image intensity probability distribution (i.e., the shape of the histogram),
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