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* Niblack's Method:<ref>{{Cite book |first=Wayne |last=Niblack |title=An introduction to digital image processing |date=1986 |publisher=Prentice-Hall International |isbn=0-13-480600-X |oclc=1244113797 }}{{pn}}</ref> Niblack's algorithm computes a local threshold for each pixel based on the mean and standard deviation of the pixel's neighborhood. It adjusts the threshold based on the local characteristics of the image, making it suitable for handling variations in illumination.
* Bernsen's Method:<ref>Chaki, Nabendu., Shaikh, Soharab Hossain., Saeed, Khalid. Exploring Image Binarization Techniques. Germany: Springer India, 2014.{{pn}}</ref> Bernsen's algorithm calculates the threshold for each pixel by considering the local contrast within a neighborhood. It uses a fixed window size and is robust to noise and variations in background intensity.
* Sauvola's Method:<ref>{{cite journal |last1=Sauvola |first1=J. |last2=Pietikäinen |first2=M. |title=Adaptive document image binarization |journal=Pattern Recognition |date=February 2000 |volume=33 |issue=2 |pages=225–236 |doi=10.1016/S0031-3203(99)00055-2 |bibcode=2000PatRe..33..225S }}</ref> Sauvola's algorithm extends Niblack's method by incorporating a dynamic factor that adapts the threshold based on the local contrast and mean intensity. This adaptive factor improves the binarization results, particularly in regions with varying contrasts.
==Extensions of binary thresholding==
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==Sources==
*<cite id=Pham2007>{{cite journal |last1=Pham |first1=Nhu-An |last2=Morrison |first2=Andrew |last3=Schwock |first3=Joerg |last4=Aviel-Ronen |first4=Sarit |last5=Iakovlev |first5=Vladimir |last6=Tsao |first6=Ming-Sound |last7=Ho |first7=James |last8=Hedley |first8=David W |title=Quantitative image analysis of immunohistochemical stains using a CMYK color model |journal=Diagnostic Pathology |date=December 2007 |volume=2 |issue=1 |page=8 |doi=10.1186/1746-1596-2-8 |doi-access=free |pmid=17326824 |pmc=1810239 }}</cite>
*<cite id=Shapiro2001> [[Linda Shapiro|Shapiro, Linda G.]] & Stockman, George C. (2002). "Computer Vision". Prentice Hall. {{ISBN|0-13-030796-3}}</cite>
*<cite id=Sezgin2004>{{cite journal |last1=Sankur |first1=Bu¨lent |title=Survey over image thresholding techniques and quantitative performance evaluation |journal=Journal of Electronic Imaging |date=2004 |volume=13 |issue=1 |pages=146 |doi=10.1117/1.1631315 |bibcode=2004JEI....13..146S }}</cite>
==Further reading==
*Gonzalez, Rafael C. & Woods, Richard E. (2002). Thresholding. In Digital Image Processing, pp. 595–611. Pearson Education. {{ISBN|81-7808-629-8}}
* {{cite journal |last1=Eichmann |first1=Marco |title=Framework for efficient optimal multilevel image thresholding |journal=Journal of Electronic Imaging |date=2009 |volume=18 |issue=1 |pages=
* {{cite journal |last1=Rosin |first1=Paul L. |title=Efficient Circular Thresholding |journal=IEEE Transactions on Image Processing |date=March 2014 |volume=23 |issue=3 |pages=992–1001 |doi=10.1109/TIP.2013.2297014 |pmid=24464614 |bibcode=2014ITIP...23..992Y |url=https://orca.cardiff.ac.uk/id/eprint/61181/ }}
*Scott E. Umbaugh (2018). Digital Image Processing and Analysis, pp 93–96. CRC Press. {{ISBN|978-1-4987-6602-9}}
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