Pattern Recognition
Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Automatic thresholding of gray-level pictures using two-dimensional entropy
Computer Vision, Graphics, and Image Processing
Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An analysis of histogram-based thresholding algorithms
CVGIP: Graphical Models and Image Processing
Maximum entropy segmentation based on the autocorrelation function of the image histogram
Journal of Computing and Information Technology
Bayesian Approaches to Gaussian Mixture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integral Ratio: A New Class of Global Thresholding Techniques for Handwriting Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evaluation of global image thresholding for change detection
Pattern Recognition Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
EM algorithm for image segmentation initialized by a tree structure scheme
IEEE Transactions on Image Processing
Adaptive thresholding by variational method
IEEE Transactions on Image Processing
Thresholding using two-dimensional histogram and fuzzy entropy principle
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Image segmentation by histogram thresholding using fuzzy sets
IEEE Transactions on Image Processing
A novel image thresholding method based on Parzen window estimate
Pattern Recognition
Computer Vision and Image Understanding
Type-2 fuzzy Gaussian mixture models
Pattern Recognition
Fractional differentiation and non-Pareto multiobjective optimization for image thresholding
Engineering Applications of Artificial Intelligence
A fast estimation method for the generalized Gaussian mixture distribution on complex images
Computer Vision and Image Understanding
A thresholding method based on two-dimensional fractional differentiation
Image and Vision Computing
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Engineering Applications of Artificial Intelligence
Unsupervised range-constrained thresholding
Pattern Recognition Letters
Ridler and Calvard's, Kittler and Illingworth's and Otsu's methods for image thresholding
Pattern Recognition Letters
Medical image thresholding using online trained neural networks
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Secure randomized image watermarking based on singular value decomposition
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
An Iterative Thresholding Segmentation Model Using a Modified Pulse Coupled Neural Network
Neural Processing Letters
Maximum similarity thresholding
Digital Signal Processing
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In this paper, a novel parametric and global image histogram thresholding method is presented. It is based on the estimation of the statistical parameters of ''object'' and ''background'' classes by the expectation-maximization (EM) algorithm, under the assumption that these two classes follow a generalized Gaussian (GG) distribution. The adoption of such a statistical model as an alternative to the more common Gaussian model is motivated by its attractive capability to approximate a broad variety of statistical behaviors with a small number of parameters. Since the quality of the solution provided by the iterative EM algorithm is strongly affected by initial conditions (which, if inappropriately set, may lead to unreliable estimation), a robust initialization strategy based on genetic algorithms (GAs) is proposed. Experimental results obtained on simulated and real images confirm the effectiveness of the proposed method.