{"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/online-mutual-foreground-segmentation-for","title":"Online Mutual Foreground Segmentation for Multispectral Stereo Videos","arxiv_id":"1809.02851","date":"2018-09-08","proceeding":null,"authors":["Pierre-Luc St-Charles","Guillaume-Alexandre Bilodeau","Robert Bergevin"],"abstract":"The segmentation of video sequences into foreground and background regions is\na low-level process commonly used in video content analysis and smart\nsurveillance applications. Using a multispectral camera setup can improve this\nprocess by providing more diverse data to help identify objects despite adverse\nimaging conditions. The registration of several data sources is however not\ntrivial if the appearance of objects produced by each sensor differs\nsubstantially. This problem is further complicated when parallax effects cannot\nbe ignored when using close-range stereo pairs. In this work, we present a new\nmethod to simultaneously tackle multispectral segmentation and stereo\nregistration. Using an iterative procedure, we estimate the labeling result for\none problem using the provisional result of the other. Our approach is based on\nthe alternating minimization of two energy functions that are linked through\nthe use of dynamic priors. We rely on the integration of shape and appearance\ncues to find proper multispectral correspondences, and to properly segment\nobjects in low contrast regions. We also formulate our model as a frame\nprocessing pipeline using higher order terms to improve the temporal coherence\nof our results. Our method is evaluated under different configurations on\nmultiple multispectral datasets, and our implementation is available online.","url_abs":"http://arxiv.org/abs/1809.02851v2","url_pdf":"http://arxiv.org/pdf/1809.02851v2.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":"online-mutual-foreground-segmentation-for","repo_url":"https://github.com/plstcharles/litiv","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"foreground-segmentation","task_name":"Foreground 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}