{"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/speed-based-filtration-and-dbscan-of-event","title":"Speed-based Filtration and DBSCAN of Event-based Camera Data with Neuromorphic Computing","arxiv_id":"2401.15212","date":"2024-01-26","proceeding":null,"authors":["Charles P. Rizzo","Catherine D. Schuman","James S. Plank"],"abstract":"Spiking neural networks are powerful computational elements that pair well with event-based cameras (EBCs). In this work, we present two spiking neural network architectures that process events from EBCs: one that isolates and filters out events based on their speeds, and another that clusters events based on the DBSCAN algorithm.","url_abs":"https://arxiv.org/abs/2401.15212v1","url_pdf":"https://arxiv.org/pdf/2401.15212v1.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":"speed-based-filtration-and-dbscan-of-event","repo_url":"https://github.com/TENNLab-UTK/dbscan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"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}