Pattern Recognition
Optimal thresholding—a new approach
Pattern Recognition Letters
A fast scheme for optimal thresholding using genetic algorithms
Signal Processing
Optimal multi-thresholding using a hybrid optimization approach
Pattern Recognition Letters
Hybrid methods using genetic algorithms for global optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid approach to modeling metabolic systems using a geneticalgorithm and simplex method
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new criterion for automatic multilevel thresholding
IEEE Transactions on Image Processing
Computer Vision and Image Understanding
Optimal multi-level thresholding using a two-stage Otsu optimization approach
Pattern Recognition Letters
An Efficient Algorithm for Optimal Multilevel Thresholding of Irregularly Sampled Histograms
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A fast estimation method for the generalized Gaussian mixture distribution on complex images
Computer Vision and Image Understanding
Engineering Applications of Artificial Intelligence
Optimal multilevel thresholding using bacterial foraging algorithm
Expert Systems with Applications: An International Journal
A review of thresholding strategies applied to human chromosome segmentation
Computer Methods and Programs in Biomedicine
Multi-level image thresholding by synergetic differential evolution
Applied Soft Computing
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This paper presented a hybrid optimal estimation algorithm for solving multi-level thresholding problems in image segmentation. The distribution of image intensity is modeled as a random variable, which is approximated by a mixture Gaussian model. The Gaussian's parameter estimates are iteratively computed by using the proposed PSO+EM algorithm, which consists of two main components: (i) global search by using particle swarm optimization (PSO); (ii) the best particle is updated through expectation maximization (EM) which leads the remaining particles to seek optimal solution in search space. In the PSO+EM algorithm, the parameter estimates fed into EM procedure are obtained from global search performed by PSO, expecting to provide a suitable starting point for EM while fitting the mixture Gaussians model. The preliminary experimental results show that the hybrid PSO+EM algorithm could solve the multi-level thresholding problem quite swiftly, and also provide quality thresholding outputs for complex images.