Thresholding (image processing): Difference between revisions

Revision as of 17:06, 18 February 2023

Revision as of 14:53, 19 February 2023

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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 |last1=Zack |first1=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 |pmid=70454 |s2cid=15339151 |issn=0022-1554|doi-access=free }}</ref> Note that these methods, more than others, make certain assumptions about the image intensity probability distribution (i.e., the shape of the histogram),

* '''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=https://ieeexplore.ieee.org/document/4310039 |journal=IEEE Transactions on Systems, Man, and Cybernetics |volume=8 |issue=8 |pages=630–632 |doi=10.1109/TSMC.1978.4310039 |issn=0018-9472}}</ref><ref>{{Cite journal |last1=Barghout |first1=L. |last2=Sheynin |first2=J. |date=2013-07-25 |title=Real-world scene perception and perceptual organization: Lessons from Computer Vision |url=http://jov.arvojournals.org/Article.aspx?doi=10.1167/13.9.709 |journal=Journal of Vision |language=en |volume=13 |issue=9 |pages=709 |doi=10.1167/13.9.709 |issn=1534-7362|doi-access=free }}</ref>

* '''[[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 |last1=Kapur |first1=J. N. |last2=Sahoo |first2=P. K. |last3=Wong |first3=A. K. C. |date=1985-03-01 |title=A new method for gray-level picture thresholding using the entropy of the histogram |url=https://dx.doi.org/10.1016/0734-189X%2885%2990125-2 |journal=Computer Vision, Graphics, and Image Processing |language=en |volume=29 |issue=3 |pages=273–285 |doi=10.1016/0734-189X(85)90125-2 |issn=0734-189X}}</ref>

* '''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.,