This repository contains the code for Comparing Deep Learning and Classical Computer Vision for Semantic Segmentation: A comprehensive analysis of cutting-edge techniques and algorithms for precise object segmentation in computer vision tasks. This work was done under the Computer Vision course at IIT Jodhpur.

GammaRay: a graphical interface to GSLib and other geomodeling algorithms. *NEW* in May, 6th: Drift analysis.

Markov random fields with covariates

Probabilistic machine learning for reconstruction and parametrization of electronic band sturcture from photoemission spectroscopy data

Predict protein complexes from high-throughput data

A high-performance general-purpose MRF MAP solver, heavily exploiting SIMD instructions.

An R package for analysis of Markov Random Fields on 2-dimensional lattices.

Conditional Auto-Regressive LASSO in R

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.

Optimized belief propagation on arbitrary graphs with binary states.

R package containing helpful functions for dealing with spatial data and concliques.

Generating continuous spatial surfaces for the manifestations of various linguistic phenomena in the Swiss-German language.

A Bayesian framework for Multi-Frame Image Super-Resolution. Based on "Bayesian Image Super-Resolution" (ME Tipping and CM Bishop, NeurIPS 2003)

A MAP-MRF Framework for Image Denoising

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.)

An implementation of "Exact Maximum A Posteriori Estimation for Binary Images" (D. Greig, B. Porteous and A. Seheult)

WASM demo of Markov random fields for image processing

[CV] Estimates depth from a single image with Markov Random Fields.

Learn a Factor Graph, or Markov Random Field (MRF), from data/observations. I.e. do PGM parameter learning.

nuclear compartmentalization, 3D genome, nuclear bodies, MRF