{"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/adaptive-morphological-reconstruction-for","title":"Adaptive Morphological Reconstruction for Seeded Image Segmentation","arxiv_id":"1904.03973","date":"2019-04-08","proceeding":null,"authors":["Tao Lei","Xiaohong Jia","Tongliang Liu","Shigang Liu","Hongy-ing Meng","Asoke K. Nandi"],"abstract":"Morphological reconstruction (MR) is often employed by seeded image\nsegmentation algorithms such as watershed transform and power watershed as it\nis able to filter seeds (regional minima) to reduce over-segmentation. However,\nMR might mistakenly filter meaningful seeds that are required for generating\naccurate segmentation and it is also sensitive to the scale because a\nsingle-scale structuring element is employed. In this paper, a novel adaptive\nmorphological reconstruction (AMR) operation is proposed that has three\nadvantages. Firstly, AMR can adaptively filter useless seeds while preserving\nmeaningful ones. Secondly, AMR is insensitive to the scale of structuring\nelements because multiscale structuring elements are employed. Finally, AMR has\ntwo attractive properties: monotonic increasingness and convergence that help\nseeded segmentation algorithms to achieve a hierarchical segmentation.\nExperiments clearly demonstrate that AMR is useful for improving algorithms of\nseeded image segmentation and seed-based spectral segmentation. Compared to\nseveral state-of-the-art algorithms, the proposed algorithms provide better\nsegmentation results requiring less computing time. Source code is available at\nhttps://github.com/SUST-reynole/AMR.","url_abs":"http://arxiv.org/abs/1904.03973v1","url_pdf":"http://arxiv.org/pdf/1904.03973v1.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":"adaptive-morphological-reconstruction-for","repo_url":"https://github.com/SUST-reynole/AMR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"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}