Image Segmentation | MRF | Potts Model | Gaussian likelihood | Bayesian| Simulated Annealing| python

Image Segmentation with hierarchical Markov Random Field with Potts Model, Bayesian inference with Gaussian likelihood and generalized Ising Priors with Simulated Annealing algorithm - an implementation in python

- Scribbles used as training data, to obtain class-conditional mean, variance and naive prior
- mean IOU was used as evaluation metric for segmentation
- Simulated Annealing initialization was random
- Grayscale input image was segmented with Bayesian MAP estimator, using Bayes Classifier (with naive class prior) and Markov Random Field (MRF) with generalized Ising (Potts) Prior (with Simulated Annealing iterative algorithm)

#imageprocessing #imageprocessingpython #python #computervision #machinelearning #algorithm #optimization #bayesian #markov #gaussian