There are 6 repositories under markov-random-field topic.
This project has two parts. In part one, we use markov random field to denoise an image. In Part two, we use similar model for image segmentation.
A high-performance general-purpose MRF MAP solver, heavily exploiting SIMD instructions.
LBP-based segmentation of defocus blur
GammaRay: a graphical interface to GSLib and other geomodeling algorithms. *NEW* in May, 6th: Drift analysis.
Image Crowd Counting Using Convolutional Neural Network and Markov Random Field
Markov random fields with covariates
A Tutorial on Modeling and Inference in Undirected Graphical Models for Hyperspectral Image Analysis
Image segmentation using the EM algorithm that relies on a GMM for intensities and a MRF model on the labels. Based on "Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm" (Zhang, Y et al.)
Framework for event- and frame-based stereo matching using SNNs and MRFs
A Bayesian framework for Multi-Frame Image Super-Resolution. Based on "Bayesian Image Super-Resolution" (ME Tipping and CM Bishop, NeurIPS 2003)
Evaluating dependencies among random variables.
Probabilistic Graphical Models final project
Factor potentials for factor graphs, Bayesian networks, and Markov random fields
A MAP-MRF Framework for Image Denoising
A package to perform EP inference in a variety of settings
Implementation of microtexture inpainting method using a probabilistic model.
This project uses MRF (Markov Random Field) to remove noise from the image and segment it.
WASM demo of Markov random fields for image processing
nuclear compartmentalization, 3D genome, nuclear bodies, MRF
This is the repository for the course Computational Imaging project with the subject: Combination of Convolutional Neural Network and Markov Random Field for image synthesis.
Predict protein complexes from high-throughput data
[CV] Estimates depth from a single image with Markov Random Fields.
An implementation of "Exact Maximum A Posteriori Estimation for Binary Images" (D. Greig, B. Porteous and A. Seheult)
R code to reproduce analyses in "Rapid winter warming could disrupt coastal marine fish community structure" (Clark et al, Nature Climate Change, 2020)
Learn a Factor Graph, or Markov Random Field (MRF), from data/observations. I.e. do PGM parameter learning.
Use Markov Random Fields to improve the basic stereo block matching algorithm