{"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/stochastic-distance-transform","title":"Stochastic Distance Transform","arxiv_id":"1810.08097","date":"2018-10-18","proceeding":null,"authors":["Johan Öfverstedt","Joakim Lindblad","Nataša Sladoje"],"abstract":"The distance transform (DT) and its many variations are ubiquitous tools for\nimage processing and analysis. In many imaging scenarios, the images of\ninterest are corrupted by noise. This has a strong negative impact on the\naccuracy of the DT, which is highly sensitive to spurious noise points. In this\nstudy, we consider images represented as discrete random sets and observe\nstatistics of DT computed on such representations. We, thus, define a\nstochastic distance transform (SDT), which has an adjustable robustness to\nnoise. Both a stochastic Monte Carlo method and a deterministic method for\ncomputing the SDT are proposed and compared. Through a series of empirical\ntests, we demonstrate that the SDT is effective not only in improving the\naccuracy of the computed distances in the presence of noise, but also in\nimproving the performance of template matching and watershed segmentation of\npartially overlapping objects, which are examples of typical applications where\nDTs are utilized.","url_abs":"http://arxiv.org/abs/1810.08097v1","url_pdf":"http://arxiv.org/pdf/1810.08097v1.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":"stochastic-distance-transform","repo_url":"https://github.com/MIDA-group/sdt","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"stochastic-distance-transform","repo_url":"https://github.com/johanofverstedt/sdt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"template-matching","task_name":"Template Matching"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}