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Event Camera Calibration of Per-pixel Biased Contrast Threshold

dc.contributor.authorWang, Ziwei
dc.contributor.authorNg, Yonhon
dc.contributor.authorvan Goor, Pieter
dc.contributor.authorMahony, Robert
dc.date.accessioned2021-07-26T02:08:31Z
dc.date.available2021-07-26T02:08:31Z
dc.date.issued2019
dc.description.abstractEvent cameras output asynchronous events to represent intensity changes with a high temporal resolution, even under extreme lighting conditions. Currently, most of the existing works use a single contrast threshold to estimate the intensity change of all pixels. However, complex circuit bias and manufacturing imperfections cause biased pixels and mismatch contrast threshold among pixels, which may lead to undesirable outputs. In this paper, we propose a new event camera model and two calibration approaches which cover event-only cameras and hybrid image-event cameras. When intensity images are simultaneously provided along with events, we also propose an efficient online method to calibrate event cameras that adapts to time-varying event rates. We demonstrate the advantages of our proposed methods compared to the state-of-the-art on several different event camera datasetsen_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn1448-2053en_AU
dc.identifier.urihttp://hdl.handle.net/1885/241072
dc.language.isoen_AUen_AU
dc.publisherAustralasian Conference on Robotics and Automationen_AU
dc.relation.ispartofAustralasian Conference on Robotics and Automation 2019, ACRA 2019, 9 December 2019 - 11 December 2019en_AU
dc.rights© 2019 The Author(s)en_AU
dc.source.urihttps://ssl.linklings.net/conferences/acra/acra2019_proceedings/views/includes/files/pap135s1-file1.pdfen_AU
dc.titleEvent Camera Calibration of Per-pixel Biased Contrast Thresholden_AU
dc.typeConference paperen_AU
dcterms.accessRightsOpen Accessen_AU
local.bibliographicCitation.lastpage11en_AU
local.bibliographicCitation.startpage1en_AU
local.contributor.affiliationWang, Ziwei, Systems Theory and Robotics Group, Australian Centre for Robotic Vision, The Australian National Universityen_AU
local.contributor.affiliationNg, Yonhon, Australian Centre for Robotic Vision, The Australian National Universityen_AU
local.contributor.affiliationvan Goor, P., Australian Centre for Robotic Vision, Research School of Electrical Engineering, The Australian National Universityen_AU
local.contributor.affiliationMahony, R., Australian Centre for Robotic Vision, The Australian National Universityen_AU
local.identifier.citationvolume2019-Decemberen_AU
local.publisher.urlhttps://ssl.linklings.net/conferences/acra/acra2019_program/views/by_date.htmlen_AU
local.type.statusAccepted Versionen_AU

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