Spatially constrained Student's \(t\)-distribution based mixture model for robust image segmentation

DOI10.1007/S10851-017-0759-8zbMath1437.94005OpenAlexW2758801564MaRDI QIDQ1799479

Abhirup Banerjee, Pradipta Maji

Publication date: 19 October 2018

Published in: Journal of Mathematical Imaging and Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10851-017-0759-8

zbMATH Keywords

segmentationhidden Markov random fieldexpectation-maximizationStudent's \(t\)-distribution

Mathematics Subject Classification ID

Point estimation (62F10) Probability distributions: general theory (60E05) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (3)

Inertial stochastic PALM and applications in machine learning ⋮ Multivariate Myriad Filters Based on Parameter Estimation of Student-$t$ Distributions ⋮ Alternatives to the EM algorithm for ML estimation of location, scatter matrix, and degree of freedom of the Student \(t\) distribution

Uses Software

• BSDS
• FSL

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Fast and robust fuzzy \(c\)-means clustering algorithms incorporating local information for image segmentation
• Spatially varying mixtures incorporating line processes for image segmentation
• New variational formulations for level set evolution without reinitialization with applications to image segmentation
• One-class support vector ensembles for image segmentation and classification
• Branch-and-mincut: global optimization for image segmentation with high-level priors
• Fuzzy connectedness image segmentation in graph cut formulation: a linear-time algorithm and a comparative analysis
• A variational framework for region-based segmentation incorporating physical noise models
• Iterative graph cuts for image segmentation with a nonlinear statistical shape prior
• A new diffusion-based variational model for image denoising and segmentation
• Color image segmentation for objects of interest with modified geodesic active contour method
• Image segmentation using a local GMM in a variational framework
• Rough Sets and Stomped Normal Distribution for Simultaneous Segmentation and Bias Field Correction in Brain MR Images
• Relative Fuzzy Connectedness among Multiple Objects: Theory, Algorithms, and Applications in Image Segmentation
• A curious likelihood identity for the multivariate t-distribution
• A Robust Fuzzy Local Information C-Means Clustering Algorithm
• Medical Image Segmentation by Combining Graph Cuts and Oriented Active Appearance Models
• Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation

This page was built for publication: Spatially constrained Student's \(t\)-distribution based mixture model for robust image segmentation