A Semi-automated Statistical Algorithm for Object Separation

TL;DR

This paper introduces a semi-automated statistical algorithm that effectively segments objects in gray scale and color images by leveraging Gaussian distribution properties and human visual sensitivity.

Contribution

It presents a novel recursive statistical method that uses Gaussian distribution characteristics and adaptive thresholds for improved object separation in images.

Findings

Effective segmentation of diverse images demonstrated

Utilizes Gaussian distribution properties for object identification

Incorporates human visual sensitivity into thresholding

Abstract

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.