Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Histogram Analysis Using a Scale-Space Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
Optimal thresholding—a new approach
Pattern Recognition Letters
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Scale-Based Detection of Corners of Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Automatic threshold selection based on histogram modes and a discriminant criterion
Machine Vision and Applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Pattern Recognition
Genetic Algorithms for Pattern Recognition
Digital Image Processing
Computer Vision and Image Understanding
The strongest schema learning GA and its application to multilevel thresholding
Image and Vision Computing
Engineering Applications of Artificial Intelligence
SAR image segmentation based on Artificial Bee Colony algorithm
Applied Soft Computing
An efficient method for segmentation of images based on fractional calculus and natural selection
Expert Systems with Applications: An International Journal
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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.