HMRF-EM-image: Implementation of the Hidden Markov Random Field Model and its Expectation-Maximization Algorithm
This provides a practical tool for researchers in image processing and computer vision, but it is incremental as it implements existing methods.
The authors implemented a MATLAB toolbox called HMRF-EM-image for 2D image segmentation using the hidden Markov random field model and expectation-maximization algorithm, with features like edge-prior-preserving segmentation and configurability for other problems such as 3D segmentation.
In this project, we study the hidden Markov random field (HMRF) model and its expectation-maximization (EM) algorithm. We implement a MATLAB toolbox named HMRF-EM-image for 2D image segmentation using the HMRF-EM framework. This toolbox also implements edge-prior-preserving image segmentation, and can be easily reconfigured for other problems, such as 3D image segmentation.