Thresholding (image processing)

Sezgin et al 2004 categorized thresholding methods into broad groups based on the information the algorithm manipulates.

[2] Note however that such a categorization is necessarily fuzzy as some methods can fall in several categories (for example, Otsu's method can be both considered a histogram-shape and a clustering algorithm) In most methods, the same threshold is applied to all pixels of an image.

However, in some cases, it can be advantageous to apply a different threshold to different parts of the image, based on the local value of the pixels.

This category of methods is called local or adaptive thresholding.

Many global thresholding methods can be adapted to work in a local way, but there are also methods developed specifically for local thresholding, such as the Niblack[7] or the Bernsen algorithms.

Software such as ImageJ propose a wide range of automatic threshold methods, both global and local.

One approach is to designate a separate threshold for each of the RGB components of the image and then combine them with an AND operation.

This reflects the way the camera works and how the data is stored in the computer, but it does not correspond to the way that people recognize color.

Most of the binary automatic thresholding methods have a natural extension for multi-thresholding.

Thresholding will work best under certain conditions : In difficult cases, thresholding will likely be imperfect and yield a binary image with false positives and false negatives.