{"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/an-efficient-iterative-thresholding-method","title":"An efficient iterative thresholding method for image segmentation","arxiv_id":"1608.01431","date":"2016-08-04","proceeding":null,"authors":["Dong Wang","Haohan Li","Xiaoyu Wei","Xiao-Ping Wang"],"abstract":"We proposed an efficient iterative thresholding method for multi-phase image\nsegmentation. The algorithm is based on minimizing piecewise constant\nMumford-Shah functional in which the contour length (or perimeter) is\napproximated by a non-local multi-phase energy. The minimization problem is\nsolved by an iterative method. Each iteration consists of computing simple\nconvolutions followed by a thresholding step. The algorithm is easy to\nimplement and has the optimal complexity $O(N \\log N)$ per iteration. We also\nshow that the iterative algorithm has the total energy decaying property. We\npresent some numerical results to show the efficiency of our method.","url_abs":"http://arxiv.org/abs/1608.01431v2","url_pdf":"http://arxiv.org/pdf/1608.01431v2.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":"an-efficient-iterative-thresholding-method","repo_url":"https://github.com/xywei/threshseg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image 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}