Thresholding (image processing): Difference between revisions

Revision as of 12:55, 8 May 2018

Revision as of 15:58, 8 December 2018

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The simplest thresholding methods replace each pixel in an image with a black pixel if the image intensity <math>I_{i,j}</math> is less than some fixed constant T (that is, <math>I_{i,j}<T</math>), or a white pixel if the image intensity is greater than that constant. In the example image on the right, this results in the dark tree becoming completely black, and the white snow becoming completely white.

==Categorizing thresholding Methodsmethods==

To make thresholding completely automated, it is necessary for the computer to automatically select the threshold T. Sezgin and Sankur (2004) categorize thresholding methods into the following six groups based on the information the algorithm manipulates [[#Sezgin2004|(Sezgin et al., 2004)]]:

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# If the difference between the previous threshold value and the new threshold value are below a specified limit, you are finished. Otherwise apply the new threshold to the original image keep trying.

=== Note about Limitslimits and Thresholdthreshold Selectionselection ===

The limit mentioned above is user definable. A larger limit will allow a greater difference between successive threshold values. Advantages of this can be quicker execution but with a less clear boundary between background and foreground. Picking starting thresholds is often done by taking the mean value of the grayscale image. However, it is also possible to pick out the starting threshold values based on the two well separated peaks of the image histogram and finding the average pixel value of those points. This can allow the algorithm to converge faster; allowing a much smaller limit to be chosen.

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== See also ==

*[[Otsu's Methodmethod]]

*[[Otsu's Method]]

*[[Balanced histogram thresholding]]

==CitationsReferences==

{{Reflist}}

==Sources==

*<cite id=Pham2007> Pham N, Morrison A, Schwock J et al. (2007). Quantitative image analysis of immunohistochemical stains using a CMYK color model. [http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&pubmedid=17326824 ''Diagn Pathol.'' '''2:'''8].</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> Mehmet Sezgin and Bulent Sankur, Survey over image thresholding techniques and quantitative performance evaluation, Journal of Electronic Imaging 13(1), 146–165 (January 2004). {{doi|10.1117/1.1631315}}</cite>

==References and furtherFurther reading==

*Gonzalez, Rafael C. & Woods, Richard E. (2002). Thresholding. In Digital Image Processing, pp.&nbsp;595&ndash;611. Pearson Education. {{ISBN|81-7808-629-8}}

*M. Luessi, M. Eichmann, G. M. Schuster, and A. K. Katsaggelos, Framework for efficient optimal multilevel image thresholding, Journal of Electronic Imaging, vol. 18, pp.&nbsp;013004+, 2009. {{doi|10.1117/1.3073891}}

*Y.K. Lai, P.L. Rosin, Efficient Circular Thresholding, IEEE Trans. on Image Processing 23(3), pp.&nbsp;992&ndash;1001 (2014). {{doi|10.1109/TIP.2013.2297014}}

*Scott E. Umbaugh (2018). Digital Image Processing and Analysis, pp 93-96. CRC Press. {{ISBN|978-1-4987-6602-9}}

==References==

{{Reflist}}

[[Category:Image segmentation]]