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 ThresholdThe simplest binariazation method is the regular thresholding, which just takes the specified threshold and separates image's pixels into black and white pixels according to the specified threshold. Although this is the simplest binarization filter, it seems to be the most useful in computer vision applications - the rest of filters are nice for image processing/enhancement applications.
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 Threshold with ErrorCarryThe filter is similar to Threshold filter in the way, that it also uses threshold value for image binarization. Unlike regular threshold filter, this filter uses cumulative pixel value in comparing with threshold value. This feature of the filter makes it more friendly to applications, which require natural representation of the source image in black and white colors. The application provides set of binarization filters bases on error diffusion. These filters are similar to binarization based on thresholding of pixels' cumulative value - each pixel is binarized based not only on its own value, but on values of some surrounding pixels.
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 Ordered ditherDithering type. Threshold filter using matrix of threshold values instead of single threshold value.
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 Bayer ordered ditherDithering type. Threshold filter using matrix of threshold values instead of single threshold value.
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 Floyed-SteinbergDiffusion type. Each pixel is binarized based not only on its own value, but on values of some surrounding pixels.
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 BurkesDiffusion type. Each pixel is binarized based not only on its own value, but on values of some surrounding pixels.
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 StuckiDiffusion type. Each pixel is binarized based not only on its own value, but on values of some surrounding pixels.
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 Jarvis-Judice-NinkeDiffusion type. Each pixel is binarized based not only on its own value, but on values of some surrounding pixels.
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 SierraDiffusion type. Each pixel is binarized based not only on its own value, but on values of some surrounding pixels.
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 SIS thresholdDithering type. Threshold filter using matrix of threshold values instead of single threshold value.
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