A Hybrid Approach Using Gaussian Smoothing and Genetic Algorithm for Multilevel Thresholding

Abstract

In this paper, a hybrid approach, which is based on Gaussian smoothing and a genetic algorithm (GA), is proposed for automatic multilevel image thresholding. Using a mixture probability density function of several Gaussian functions to fit an image histogram and then find the optimal threshold(s) is a well-known optimal thresholding method. In the proposed approach, the Gaussian kernel smoothing is used to estimate the number of classes in an image. Since the parameter estimation in the method is typically a nonlinear optimization problem, the parameters used in the mixture of Gaussian functions that give the best fit to the processed histogram are determined using GA. In experiments, synthetic data and real images were processed to evaluate the thresholding performance. The experimental results to confirm the proposed approach are also included.