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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 |
* '''Clustering'''-based methods, where the gray-level samples are clustered in two parts as background and foreground,<ref>{{Cite journal |date=1978 |title=Picture Thresholding Using an Iterative Selection Method |url=
* '''[[Entropy (information theory)|Entropy]]'''-based methods result in algorithms that use the entropy of the foreground and background regions, the cross-entropy between the original and binarized image, etc.,<ref>{{Cite journal |
* '''Object Attribute'''-based methods search a measure of similarity between the gray-level and the binarized images, such as fuzzy shape similarity, edge coincidence, etc.,
* '''Spatial''' methods use higher-order probability distribution and/or correlation between pixels.
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=== Multi-band images ===
Color images can also be thresholded. One approach is to designate a separate threshold for each of the [[RGB color model|RGB]] components of the image and then combine them with an [[Binary and|AND]] operation. This reflects the way the camera works and how the data is stored in the computer, but it does not correspond to the way that people recognize color. Therefore, the [[HSL and HSV]] color models are more often used; note that since [[hue]] is a circular quantity it requires [[circular thresholding]]. It is also possible to use the [[CMYK color model|CMYK]] color model.<ref>{{Cite journal |
=== Multiple thresholds ===
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