  We explicate a semi-automated statistical algorithm for object identification
and segregation in both gray scale and color images. The algorithm makes
optimal use of the observation that definite objects in an image are typically
represented by pixel values having narrow Gaussian distributions about
characteristic mean values. Furthermore, for visually distinct objects, the
corresponding Gaussian distributions have negligible overlap with each other
and hence the Mahalanobis distance between these distributions are large. These
statistical facts enable one to sub-divide images into multiple thresholds of
variable sizes, each segregating similar objects. The procedure incorporates
the sensitivity of human eye to the gray pixel values into the variable
threshold size, while mapping the Gaussian distributions into localized
\delta-functions, for object separation. The effectiveness of this recursive
statistical algorithm is demonstrated using a wide variety of images.
