Thresholding (image processing): Difference between revisions

Revision as of 04:54, 27 August 2024

Latest revision as of 16:19, 12 August 2025

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=== Examples of Algorithms for Local Thresholding ===

* 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|datepages=April115-116 2024}}</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|date=April 2024}}</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.