Continuous-Time Intensity Estimation Using Event Cameras

dc.contributor.authorScheerlinck, Cedric
dc.contributor.authorBarnes, Nick
dc.contributor.authorMahony, Robert
dc.contributor.editorJawahar, C
dc.contributor.editorLi, H
dc.contributor.editorMori, G
dc.contributor.editorSchindler, K
dc.coverage.spatialPerth, Australia
dc.date.accessioned2024-04-18T04:16:48Z
dc.date.createdDecember 2-6 2018
dc.date.issued2019
dc.date.updated2022-12-18T07:16:20Z
dc.description.abstractEvent 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.sponsorshipThis 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.mimetypeapplication/pdfen_AU
dc.identifier.isbn978-3-030-20886-8en_AU
dc.identifier.urihttp://hdl.handle.net/1885/316884
dc.language.isoen_AUen_AU
dc.provenancehttps://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.publisherSpringeren_AU
dc.relationhttp://purl.org/au-research/grants/arc/CE140100016en_AU
dc.relation.ispartofseries14th Asian Conference on Computer Vision (ACCV 2018)en_AU
dc.rights© 2019 Springer Nature Switzerland AG 2019en_AU
dc.sourceProceedings of the 14th Asian Conference on Computer Vision LNCSen_AU
dc.titleContinuous-Time Intensity Estimation Using Event Camerasen_AU
dc.typeConference paperen_AU
dcterms.accessRightsOpen Access
local.bibliographicCitation.lastpage324en_AU
local.bibliographicCitation.startpage308en_AU
local.contributor.affiliationScheerlinck, Cedric, College of Engineering, Computing and Cybernetics, ANUen_AU
local.contributor.affiliationBarnes, Nick, College of Engineering, Computing and Cybernetics, ANUen_AU
local.contributor.affiliationMahony, Robert, College of Engineering, Computing and Cybernetics, ANUen_AU
local.contributor.authoruidScheerlinck, Cedric, u6287914en_AU
local.contributor.authoruidBarnes, Nick, u4591576en_AU
local.contributor.authoruidMahony, Robert, u4033888en_AU
local.description.notesImported from ARIESen_AU
local.description.refereedYes
local.identifier.absfor460300 - Computer vision and multimedia computationen_AU
local.identifier.ariespublicationu5786633xPUB1881en_AU
local.identifier.doi10.1007/978-3-030-20873-8_20en_AU
local.identifier.scopusID2-s2.0-85066800372
local.identifier.thomsonIDWOS:000492904000020
local.publisher.urlhttps://link.springer.com/en_AU
local.type.statusAccepted Versionen_AU

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