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dc.contributor.authorSariyanidi, Evangelos
dc.contributor.authorGunes, Hatice
dc.contributor.authorCavallaro, Andrea
dc.date.accessioned2018-03-20T15:06:23Z
dc.date.available2018-03-20T15:06:23Z
dc.date.issued2017-03
dc.identifier.issn1057-7149
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/274130
dc.description.abstractThe growing use of cameras embedded in autonomous robotic platforms and worn by people is increasing the importance of accurate global motion estimation (GME). However, existing GME methods may degrade considerably under illumination variations. In this paper, we address this problem by proposing a biologically-inspired GME method that achieves high estimation accuracy in the presence of illumination variations. We mimic the early layers of the human visual cortex with the spatio-temporal Gabor motion energy by adopting the pioneering model of Adelson and Bergen and we provide the closed-form expressions that enable the study and adaptation of this model to different application needs. Moreover, we propose a normalisation scheme for motion energy to tackle temporal illumination variations. Finally, we provide an overall GME scheme which, to the best of our knowledge, achieves the highest accuracy on the Pose, Illumination, and Expression (PIE) database.
dc.languageeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.titleBiologically-Inspired Motion Encoding for Robust Global Motion Estimation.
dc.typeArticle
prism.endingPage1535
prism.issueIdentifier3
prism.publicationDate2017
prism.publicationNameIEEE Trans Image Process
prism.startingPage1521
prism.volume26
dc.identifier.doi10.17863/CAM.21213
dcterms.dateAccepted2016-12-14
rioxxterms.versionofrecord10.1109/TIP.2017.2651394
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2017-03
dc.contributor.orcidGunes, Hatice [0000-0003-2407-3012]
dc.identifier.eissn1941-0042
rioxxterms.typeJournal Article/Review
cam.issuedOnline2017-01-24


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