{"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/gmm-based-hidden-markov-random-field-for","title":"GMM-Based Hidden Markov Random Field for Color Image and 3D Volume Segmentation","arxiv_id":"1212.4527","date":"2012-12-18","proceeding":null,"authors":["Quan Wang"],"abstract":"In this project, we first study the Gaussian-based hidden Markov random field\n(HMRF) model and its expectation-maximization (EM) algorithm. Then we\ngeneralize it to Gaussian mixture model-based hidden Markov random field. The\nalgorithm is implemented in MATLAB. We also apply this algorithm to color image\nsegmentation problems and 3D volume segmentation problems.","url_abs":"http://arxiv.org/abs/1212.4527v1","url_pdf":"http://arxiv.org/pdf/1212.4527v1.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":"gmm-based-hidden-markov-random-field-for","repo_url":"https://github.com/wq2012/GMM-HMRF","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":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}