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Vladsimontov (talk | contribs) Added quick section on Automatic Thresholding Tags: nowiki added Visual edit |
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Histogram shape-based methods in particular, but also many other thresholding algorithms, make certain assumptions about the image intensity probability distribution. The most common thresholding methods work on bimodal distributions, but algorithms have also been developed for [[Unimodal thresholding|unimodal distributions]], multimodal distributions, and [[Circular thresholding|circular distributions]].
== Automatic Thresholding ==
Automatic Thresholding is a great way to extract useful information encoded into pixels while minimizing background noise. This is accomplished by utilizing a feedback loop to optimize the threshold value before converting the original grayscale image to binary. The idea is to separate the image into two parts; the background and foreground.
# Select initial threshold value, typically the mean 8-bit value of the original image.
# Divide the original image into two portions;
## Pixel values that are less than or equal to the threshold; background
## Pixel values greater than the threshold; foreground
# Find the average mean values of the two new images
# Calculate the new threshold by averaging the two means.
# 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 Limits and Threshold Selection ===
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.
=== Method Limitations ===
Automatic thresholding will work best when a good background to foreground contrast ratio exists. Meaning the picture must be taken in good lighting conditions with minimal glare.
== See also ==
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*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. 013004+, 2009. {{doi|10.1117/1.3073891}}
*Y.K. Lai, P.L. Rosin, Efficient Circular Thresholding, IEEE Trans. on Image Processing 23(3), pp. 992–1001 (2014). {{doi|10.1109/TIP.2013.2297014}}
*Scott E. Umbaugh (2018). Digital Image Processing and Analysis, pp 93-96. CRC Press. <nowiki>ISBN 978-1-4987-6602-9</nowiki>
==References==
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