Feature guided Gaussian mixture model with semi-supervised EM and local geometric constraint for retinal image registration

DOI10.1016/J.INS.2017.07.010zbMath1444.92054OpenAlexW2735299109MaRDI QIDQ778449

Yansheng Li, Chengyin Liu, Junjun Jiang, Jiayi Ma

Publication date: 2 July 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2017.07.010

zbMATH Keywords

GMMfeature guidedlocal geometric constraintretina image registrationsemi-supervised EM

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55)

Related Items (4)

Locality preserving matching ⋮ Multivariate Bounded Asymmetric Gaussian Mixture Model ⋮ A non-parametric depth modification model for registration between color and depth images ⋮ Image matching from handcrafted to deep features: a survey

Uses Software

• SIFT
• SIRF
• SURF

Cites Work

• Regularized vector field learning with sparse approximation for mismatch removal
• Optic disk feature extraction via modified deformable model technique for glaucoma analysis
• Text classification from labeled and unlabeled documents using EM
• Multidimensional binary search trees used for associative searching
• A new point matching algorithm for non-rigid registration
• Robust Point Matching via Vector Field Consensus
• SIRF: Simultaneous Satellite Image Registration and Fusion in a Unified Framework
• Non-Rigid Point Set Registration by Preserving Global and Local Structures
• Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples
• On Learning Vector-Valued Functions
• Unnamed Item

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