{"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/motion-blur-filtering-a-statistical-approach","title":"Motion Blur Filtering: A Statistical Approach for Extracting Confinement Forces and Diffusivity from a Single Blurred Trajectory","arxiv_id":"1510.06062","date":"2016-05-18","proceeding":null,"authors":[],"abstract":"Single Particle Tracking (SPT) can aid in understanding complex\nspatio-temporal processes. However, quantifying diffusivity and forces from\nindividual live cell trajectories is complicated by inter- & intra-trajectory\nkinetic heterogeneity, thermal fluctuations, and statistical temporal\ndependence inherent to the underlying molecule's time correlated confined\ndynamics experienced in the cell. Experimental artifacts such as localization\nuncertainty and motion blur also obscure the data. We introduce a new maximum\nlikelihood estimation (MLE) technique that decouples the above noise sources\nand systematically treats temporal correlation via a likelihood function\n(permitting more reliable extraction of effective forces from position vs. time\ndata). Our estimator is demonstrated to be consistent over a wide range of\nexposure times, diffusion coefficients, and confinement \"radii\". The algorithm\nand corresponding software can reliably extract motion parameters independent\nof exposure time in trajectories exhibiting confined and/or non-stationary\ndynamics and will aid in directly comparing trajectories obtained from\ndifferent imaging modalities.","url_abs":"http://arxiv.org/abs/1510.06062v2","url_pdf":"http://arxiv.org/pdf/1510.06062v2.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":"motion-blur-filtering-a-statistical-approach","repo_url":"https://github.com/calderoc/MotionBlurFilter","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}