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dc.contributor.authorYi, Dewei
dc.contributor.authorSu, Jinya
dc.contributor.authorChen, Wen Hua
dc.date.accessioned2022-06-29T23:07:52Z
dc.date.available2022-06-29T23:07:52Z
dc.date.issued2021-10-07
dc.identifier.citationYi , D , Su , J & Chen , W H 2021 , ' Probabilistic Faster R-CNN with Stochastic Region Proposing : Towards Object Detection and Recognition in Remote Sensing Imagery ' , Neurocomputing , vol. 459 , pp. 290-301 . https://doi.org/10.1016/j.neucom.2021.06.072en
dc.identifier.issn0925-2312
dc.identifier.otherPURE: 196025436
dc.identifier.otherPURE UUID: 88c41d40-a117-4eff-ac20-a416d61791ff
dc.identifier.otherScopus: 85109592968
dc.identifier.otherORCID: /0000-0003-1702-9136/work/96970863
dc.identifier.otherScopus: 85109592968
dc.identifier.otherORCID: /0000-0002-3121-7208/work/112496163
dc.identifier.urihttps://hdl.handle.net/2164/18762
dc.descriptionFunding Information: This work was supported by the U.K. Science and Technology Facilities Council (STFC) Enabling Wide Area Persistent Remote Sensing for Agriculture Applications by Developing and Coordinating Multiple Heterogeneous Platforms programme and Integrating Advanced Earth Observation and Environmental Information for Sustainable Management of Crop Pests and Diseases programme under grant number ST/N006852/1 and ST/N006712/1.en
dc.format.extent12
dc.language.isoeng
dc.relation.ispartofNeurocomputingen
dc.rights© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectObject detectionen
dc.subjectImage recognitionen
dc.subjectGaussian mixture modelsen
dc.subjectRegion proposal networken
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectComputer Science Applicationsen
dc.subjectCognitive Neuroscienceen
dc.subjectArtificial Intelligenceen
dc.subjectSTFC - Science and Technology Facilities Councilen
dc.subjectST/N006852/1en
dc.subjectST/N006712/1en
dc.subject.lccQA75en
dc.titleProbabilistic Faster R-CNN with Stochastic Region Proposing : Towards Object Detection and Recognition in Remote Sensing Imageryen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.description.statusPeer revieweden
dc.description.versionPostprinten
dc.identifier.doihttps://doi.org/10.1016/j.neucom.2021.06.072
dc.date.embargoedUntil2022-06-30
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85109592968&partnerID=8YFLogxKen


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