Otsu Thresholding Why 'minimum within class variance' gives the optimum threshold?

1. What is the Otsu's method main principle for calculating the optimal threshold?
2. What is Otsu's threshold?
3. What is meant by optimal thresholding?
4. What is optimal thresholding in image processing?

What is the Otsu's method main principle for calculating the optimal threshold?

Otsu's method[1] is a variance-based technique to find the threshold value where the weighted variance between the foreground and background pixels is the least. The key idea here is to iterate through all the possible values of threshold and measure the spread of background and foreground pixels.

What is Otsu's threshold?

Otsu's thresholding method involves iterating through all the possible threshold values and calculating a measure of spread for the pixel levels each side of the threshold, i.e. the pixels that either fall in foreground or background.

What is meant by optimal thresholding?

• Optimal thresholding

- Suppose that an image contains only two principal regions (e.g., object and back- ground) - We can minimize the number of misclassified pixels if we have some prior knowl- edge about the distributions of the gray level values that make up the object and the background.

What is optimal thresholding in image processing?

In this method, we find the spread of foreground and background of the pictures for all possible values of threshold. The threshold with the least spread is taken as the optimal threshold.