Image segmentation using a trimmed likelihood estimator in the asymmetric mixture model based on generalized gamma and Gaussian distributions

DOI10.1155/2018/3468967zbMath1427.94034OpenAlexW2792825441MaRDI QIDQ1720766

Xianqiang Yang

Publication date: 8 February 2019

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2018/3468967

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15) Generalized linear models (logistic models) (62J12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (3)

Main probabilistic characteristics of the digamma distribution and the method of estimating its parameters ⋮ An improved multithreshold segmentation algorithm based on graph cuts applicable for irregular image ⋮ Segmentation of brain MR images by using fully convolutional network and Gaussian mixture model with spatial constraints

Cites Work

• Unnamed Item
• Robust fitting of mixtures using the trimmed likelihood estimator
• Bayesian learning of finite generalized Gaussian mixture models on images
• Breakdown points of trimmed likelihood estimators and related estimators in generalized linear models.
• A new condition for identifiability of finite mixture distributions
• Finite mixture models
• Gaussian Mixture Models Reduction by Variational Maximum Mutual Information

This page was built for publication: Image segmentation using a trimmed likelihood estimator in the asymmetric mixture model based on generalized gamma and Gaussian distributions