Bayesian expectation maximization algorithm by using B-splines functions: application in image segmentation

DOI10.1016/j.matcom.2015.06.007OpenAlexW922536346MaRDI QIDQ2228742

Atizez Hadrich, Afif Masmoudi, Mourad Zribi

Publication date: 19 February 2021

Published in: Mathematics and Computers in Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.matcom.2015.06.007

zbMATH Keywords

image segmentationmixture densityexpectation maximization algorithmBayesian estimatorB-splines functions

Mathematics Subject Classification ID

Nonparametric inference (62Gxx) Computing methodologies and applications (68Uxx)

Related Items (max. 100)

Nonparametric density estimation with nonuniform B-spline bases ⋮ A non-intrusive B-splines Bézier elements-based method for uncertainty propagation

Cites Work

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• Convergence properties of MLE's and asymptotic simultaneous confidence intervals in fitting cardinal B-splines for density estimation
• Bivariate B-splines for tensor logspline density estimation
• Approximation by \(B\)-spline convolution operators. A probabilistic approach
• On Polya frequency functions. IV: The fundamental spline functions and their limits
• Remarks on Some Nonparametric Estimates of a Density Function
• Nonparametric probability density estimation using normalized b–splines
• Univariate density estimation by orthogonal series
• Convergence Rates for Uniform B-Spline Density Estimators Part I: One Dimension
• Historical Development of the Newton–Raphson Method
• Convergence rates for uniform B-spline density estimators on bounded and semi-infinite domains

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