{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hmrf-em-image-implementation-of-the-hidden","title":"HMRF-EM-image: Implementation of the Hidden Markov Random Field Model and its Expectation-Maximization Algorithm","arxiv_id":"1207.3510","date":"2012-07-15","proceeding":null,"authors":["Quan Wang"],"abstract":"In this project, we study the hidden Markov random field (HMRF) model and its\nexpectation-maximization (EM) algorithm. We implement a MATLAB toolbox named\nHMRF-EM-image for 2D image segmentation using the HMRF-EM framework. This\ntoolbox also implements edge-prior-preserving image segmentation, and can be\neasily reconfigured for other problems, such as 3D image segmentation.","url_abs":"http://arxiv.org/abs/1207.3510v2","url_pdf":"http://arxiv.org/pdf/1207.3510v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hmrf-em-image-implementation-of-the-hidden","repo_url":"https://github.com/wq2012/HMRF-EM-image","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}