  A novel algorithm is proposed for segmenting an image into multiple levels
using its mean and variance. Starting from the extreme pixel values at both
ends of the histogram plot, the algorithm is applied recursively on sub-ranges
computed from the previous step, so as to find a threshold level and a new
sub-range for the next step, until no significant improvement in image quality
can be achieved. The method makes use of the fact that a number of
distributions tend towards Dirac delta function, peaking at the mean, in the
limiting condition of vanishing variance. The procedure naturally provides for
variable size segmentation with bigger blocks near the extreme pixel values and
finer divisions around the mean or other chosen value for better visualization.
Experiments on a variety of images show that the new algorithm effectively
segments the image in computationally very less time.
