Continuous-Time Intensity Estimation Using Event Cameras
| dc.contributor.author | Scheerlinck, Cedric | |
| dc.contributor.author | Barnes, Nick | |
| dc.contributor.author | Mahony, Robert | |
| dc.contributor.editor | Jawahar, C | |
| dc.contributor.editor | Li, H | |
| dc.contributor.editor | Mori, G | |
| dc.contributor.editor | Schindler, K | |
| dc.coverage.spatial | Perth, Australia | |
| dc.date.accessioned | 2024-04-18T04:16:48Z | |
| dc.date.created | December 2-6 2018 | |
| dc.date.issued | 2019 | |
| dc.date.updated | 2022-12-18T07:16:20Z | |
| dc.description.abstract | Event cameras provide asynchronous, data-driven measurements of local temporal contrast over a large dynamic range with extremely high temporal resolution. Conventional cameras capture low frequency reference intensity information. These two sensor modalities provide complementary information. We propose a computationally efficient, asynchronous filter that continuously fuses image frames and events into a single high-temporal-resolution, high-dynamic-range image state. In absence of conventional image frames, the filter can be run on events only. We present experimental results on high-speed, highdynamic-range sequences, as well as on new ground truth datasets we generate to demonstrate the proposed algorithm outperforms existing state-of-the-art methods. | en_AU |
| dc.description.sponsorship | This research was supported by an Australian Government Research Training Program Scholarship, and the Australian Research Council through the “Australian Centre of Excellence for Robotic Vision” CE140100016. | en_AU |
| dc.format.mimetype | application/pdf | en_AU |
| dc.identifier.isbn | 978-3-030-20886-8 | en_AU |
| dc.identifier.uri | http://hdl.handle.net/1885/316884 | |
| dc.language.iso | en_AU | en_AU |
| dc.provenance | https://www.springernature.com/gp/open-research/policies/book-policies..."Accepted version of proceedings papers can be archived in institutional repository, 12 months embargo" from the publisher site (as at 6 May 2024) | |
| dc.publisher | Springer | en_AU |
| dc.relation | http://purl.org/au-research/grants/arc/CE140100016 | en_AU |
| dc.relation.ispartofseries | 14th Asian Conference on Computer Vision (ACCV 2018) | en_AU |
| dc.rights | © 2019 Springer Nature Switzerland AG 2019 | en_AU |
| dc.source | Proceedings of the 14th Asian Conference on Computer Vision LNCS | en_AU |
| dc.title | Continuous-Time Intensity Estimation Using Event Cameras | en_AU |
| dc.type | Conference paper | en_AU |
| dcterms.accessRights | Open Access | |
| local.bibliographicCitation.lastpage | 324 | en_AU |
| local.bibliographicCitation.startpage | 308 | en_AU |
| local.contributor.affiliation | Scheerlinck, Cedric, College of Engineering, Computing and Cybernetics, ANU | en_AU |
| local.contributor.affiliation | Barnes, Nick, College of Engineering, Computing and Cybernetics, ANU | en_AU |
| local.contributor.affiliation | Mahony, Robert, College of Engineering, Computing and Cybernetics, ANU | en_AU |
| local.contributor.authoruid | Scheerlinck, Cedric, u6287914 | en_AU |
| local.contributor.authoruid | Barnes, Nick, u4591576 | en_AU |
| local.contributor.authoruid | Mahony, Robert, u4033888 | en_AU |
| local.description.notes | Imported from ARIES | en_AU |
| local.description.refereed | Yes | |
| local.identifier.absfor | 460300 - Computer vision and multimedia computation | en_AU |
| local.identifier.ariespublication | u5786633xPUB1881 | en_AU |
| local.identifier.doi | 10.1007/978-3-030-20873-8_20 | en_AU |
| local.identifier.scopusID | 2-s2.0-85066800372 | |
| local.identifier.thomsonID | WOS:000492904000020 | |
| local.publisher.url | https://link.springer.com/ | en_AU |
| local.type.status | Accepted Version | en_AU |
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