This iterative technique for choosing a threshold was developed by Ridler and Calvard .The histogram is initially segmented into two parts using a starting threshold value such as 0 = 2B-1, half the maximum dynamic range.
The sample mean (mf,0) of the gray values associated with the foreground pixels and the sample mean (mb,0) of the gray values associated with the background pixels are computed. A new threshold value 1 is now computed as the average of these two sample means. The process is repeated, based upon the new threshold, until the threshold value does not change any more.
Reference :T.W. Ridler, S. Calvard, Picture thresholding using an iterative selection method, IEEE Trans. System, Man and Cybernetics, SMC-8 (1978) 630-632.
zephyr (2022). Automatic Thresholding (https://www.mathworks.com/matlabcentral/fileexchange/3195-automatic-thresholding), MATLAB Central File Exchange. Retrieved .
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