We’ll be discussing simple thresholding and Otsu’s thresholding here today. And finally we have adaptive thresholding which, instead of trying to threshold an image globally using a single value, instead breaks the image down into smaller pieces, and thresholds each of these pieces separately and individually.We also have methods such as Otsu’s thresholding that attempt to be more dynamic and automatically compute the optimal threshold value based on the input image.We have simple thresholding where we manually supply parameters to segment the image - this works extremely well in controlled lighting conditions where we can ensure high contrast between the foreground and background of the image.Thresholding is one of the most common (and basic) segmentation techniques in computer vision and it allows us to separate the foreground (i.e., the objects that we are interested in) from the background of the image.
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