{"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/quasiperiodicity-quantification-in-video-data","title":"(Quasi)Periodicity Quantification in Video Data, Using Topology","arxiv_id":"1704.08382","date":"2017-04-26","proceeding":null,"authors":["Christopher J. Tralie","Jose A. Perea"],"abstract":"This work introduces a novel framework for quantifying the presence and\nstrength of recurrent dynamics in video data. Specifically, we provide\ncontinuous measures of periodicity (perfect repetition) and quasiperiodicity\n(superposition of periodic modes with non-commensurate periods), in a way which\ndoes not require segmentation, training, object tracking or 1-dimensional\nsurrogate signals. Our methodology operates directly on video data. The\napproach combines ideas from nonlinear time series analysis (delay embeddings)\nand computational topology (persistent homology), by translating the problem of\nfinding recurrent dynamics in video data, into the problem of determining the\ncircularity or toroidality of an associated geometric space. Through extensive\ntesting, we show the robustness of our scores with respect to several noise\nmodels/levels, we show that our periodicity score is superior to other methods\nwhen compared to human-generated periodicity rankings, and furthermore, we show\nthat our quasiperiodicity score clearly indicates the presence of biphonation\nin videos of vibrating vocal folds, which has never before been accomplished\nend to end quantitatively.","url_abs":"http://arxiv.org/abs/1704.08382v2","url_pdf":"http://arxiv.org/pdf/1704.08382v2.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":"quasiperiodicity-quantification-in-video-data","repo_url":"https://github.com/ctralie/SlidingWindowVideoTDA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.08382","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}