dc.contributorCETYS Universidad
dc.creatorPicos, Kenia
dc.creatorOrozco Rosas, Ulises
dc.creatorDíaz Ramírez, Víctor H.
dc.date.accessioned2020-09-14T18:40:15Z
dc.date.accessioned2022-10-14T15:40:45Z
dc.date.available2020-09-14T18:40:15Z
dc.date.available2022-10-14T15:40:45Z
dc.date.created2020-09-14T18:40:15Z
dc.date.issued2019-09-06
dc.identifier9781510629653
dc.identifierhttps://repositorio.cetys.mx/handle/60000/873
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4254987
dc.description.abstractThis paper proposes frequency-domain correlation filtering to solve object recognition of three-dimensional (3D) targets. We perform a linear correlation in the frequency domain between an input frame of the video sequence and a designed filter. This operation measures the correspondence between the two signals. In order to produce a high matching score, we design a bank of correlation filters, in which each filter contains unique information of the target in a single view and statistical parameters of the scene. In this paper, we demonstrate the feasibility of correlation filters used to solve 3D object recognition and their robustness to different image conditions such as noise, cluttered background, and geometrical distortions of the target. The evaluation performance presents a high accuracy in terms of quantitative metrics.
dc.languageen
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/2.5/mx/
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 México
dc.subjectCorrelation filters
dc.subjectFrequency domain filtering
dc.subjectObject recognition
dc.subjectThree-dimensional estimation
dc.titleDemonstrating the robustness of frequency-domain correlation filters for 3D object recognition applications
dc.typePresentation


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