This is the first 1,000 characters of 784 words (3.14 pages) in the essay titled Adaptive Thresholding
We have to develop an adaptive thresholding system for greyscale image binarisation. The simplest way to use image binarisation is to choose a threshold value, and classify all pixels with values above this threshold value as white and all other pixels as black. Thresholding essentially involves turning a colour or greyscale image into a 1-bit binary image. If, say, the left half of an image had a lower brightness range than the right half, we make use of Adaptive Thresholding. Global thresholding uses a fixed threshold for all pixels in the image and therefore works only if the intensity histogram of the input image contains distinct peaks corresponding to the desired subject and background. Hence, it cannot deal with images containing, for example, a strong illumination gradient.
Local adaptive thresholding, on the other hand, selects an individual threshold for each pixel based on the range of intensity values in its local neighbourhood. This a...
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