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

Article type: Research Article

Authors: Lai, Chih-Chin^a | Tseng, Din-Chang^b

Affiliations: [a] Dept. of Computer Science and Information Engineering, National University of Tainan, Tainan, Taiwan 700. [email protected] | [b] Dept. of Computer Science and Information Engineering, National Central University, Chung-li, Taiwan 320

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.

Keywords: image segmentation, optimal thresholding, parameter estimation, genetic algorithm

DOI: 10.3233/HIS-2004-13-403

Journal: International Journal of Hybrid Intelligent Systems, vol. 1, no. 3-4, pp. 143-152, 2004