Evaluation of the robustness of deep features on the change detection problem

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dc.contributor.authorÖztimur Karadağ, Özge
dc.contributor.authorErdaş, Özlem
dc.date.accessioned2021-02-19T21:16:16Z
dc.date.available2021-02-19T21:16:16Z
dc.date.issued2018
dc.departmentALKÜ
dc.description26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEY
dc.description.abstractDeep Learning is a method which is employed for change detection as well as other image processing problems. Output extracted from various layers of the deep architecture can be employed to detect changes at different scales. In this study, output extracted from the layers of deep architecture is referred as deep features and the robustness of these features on the change detection problem are evaluated experimentally. As a result, it is observed that deep features, when used alone, could detect the change in images with steady background successfully but they were sensitive to dynamic background and camera jitter.
dc.description.sponsorshipIEEE, Huawei, Aselsan, NETAS, IEEE Turkey Sect, IEEE Signal Proc Soc, IEEE Commun Soc, ViSRATEK, Adresgezgini, Rohde & Schwarz, Integrated Syst & Syst Design, Atilim Univ, Havelsan, Izmir Katip Celebi Univ
dc.identifier.isbn978-1-5386-1501-0
dc.identifier.issn2165-0608
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12868/351
dc.identifier.wosWOS:000511448500489
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor0-belirlenecek
dc.language.isotr
dc.publisherIeee
dc.relation.ispartof2018 26Th Signal Processing And Communications Applications Conference (Siu)
dc.relation.ispartofseriesSignal Processing and Communications Applications Conference
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectchange detection
dc.subjectdeep learning
dc.subjectdeep features
dc.titleEvaluation of the robustness of deep features on the change detection problem
dc.typeConference Object

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