Spatially varying mixtures incorporating line processes for image segmentation

DOI10.1007/S10851-009-0174-XzbMath1490.94027OpenAlexW1970536602MaRDI QIDQ993559

Christian Heinrich, Christophoros Nikou, Nikolaos Galatsanos, Giorgos Sfikas

Publication date: 20 September 2010

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

Full work available at URL: https://doi.org/10.1007/s10851-009-0174-x

zbMATH Keywords

image segmentationmaximum a posteriori estimationexpectation-maximizationvariational inferenceedge-preservationGauss-Markov random fieldspatially varying Gaussian mixture model

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Image analysis in multivariate analysis (62H35) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (4)

Multivariate Myriad Filters Based on Parameter Estimation of Student-$t$ Distributions ⋮ Variational-Bayes optical flow ⋮ Spatially constrained Student's \(t\)-distribution based mixture model for robust image segmentation ⋮ Tomographic image reconstruction with a spatially varying gamma mixture prior

Uses Software

• BSDS
• PRMLT

Cites Work

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• EM procedures using mean field-like approximations for Markov model-based image segmentation
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Numerical Optimization
• Finite mixture models

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