Optimal thresholding—a new approach
Pattern Recognition Letters
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Optimal multi-thresholding using a hybrid optimization approach
Pattern Recognition Letters
On the modeling of DCT and subband image data for compression
IEEE Transactions on Image Processing
Fast adaptive learning algorithm for sub-band adaptive thresholding function in image denoising
International Journal of Computational Intelligence Studies
Median-based image thresholding
Image and Vision Computing
Ridler and Calvard's, Kittler and Illingworth's and Otsu's methods for image thresholding
Pattern Recognition Letters
Color image segmentation using gaussian mixtures and particle swarm optimization
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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The generalized Gaussian distribution (GGD) mixture model is a parametric statistical model, which is frequently employed to characterize the statistical behavior of a process signal in industry. This paper considers the GGD mixture model to approximate the empirical distributions, especially for those arising from non-Gaussian sources. A new estimation method is developed for fitting the GGD mixture model. The proposed method integrates Particle Swarm Optimization (PSO) from Computational Intelligence and Entropy Matching Estimator (EME) from Statistical Computation to seek the optimal parameter estimates, particularly when there is at least one large shape parameter in the GGD mixture model. Thus, the method is termed PSO+EME. Applications to multi-level thresholding in image processing are used to illustrate PSO+EME. Image thresholding is a useful technique to separate the interested object from background information. Due to the versatility of the GGD mixture model in characterizing process signals, it is chosen to fit the intensity of image and PSO+EME is used to estimate the parameters. The experimental study shows that the fitted model produced by PSO+EME could depicts quite successfully the non-Gaussian probability density function of image intensity, and therefore present quality effectiveness in the problem of multi-level thresholding.