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Thresholding
From Computer Vision Wiki
In a binary image 0 is black and 1 is white. A gray scale image can represented by an array of numbers between 0 and 1. In practice, however, gray scale images are tables of integers. The integers normally run from 0 (black) to 255 (white).
These numbers together form the gray scale function of the image. An example is on the right.
The image on the left is accompanied by its gray scale function. Higher means lighter and lower means darker.
Now recall our approach:
Thresholding is the following procedure that creates binary images from the gray scale image. We call these images frames:
Given a number, threshold, T between 0 and 255, create a T-th frame by replacing all the pixels with gray level lower than or equal to T with black (0), the rest with white (1).
Converting a gray scale image to a binary image is a common technique in image analysis. By thresholding of a gray scale image normally is understood the following procedure. Given a number, threshold, T between 0 and 255, create the binary image by replacing all the pixels with gray level in the original image lower than or equal to T with black.
As you raise the threshold, the number of black pixels increases, as in the images above.
Another illustration of thresholding is here [1].
The methods of finding the best threshold are the subject of an enormous amount of research. We will not deal with this issue. Instead we threshold the image for each possible value of T and accept the resulting sequence of binary images as a representation of the original.
Color Images can also be thresholded...
