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dc.contributor.authorFernández Fabeiro, Jorge 
dc.contributor.authorOrdóñez, Álvaro
dc.contributor.authorGonzález Escribano, Arturo 
dc.contributor.authorBlanco Heras, Dora
dc.date.accessioned2018-10-17T10:15:22Z
dc.date.available2018-10-17T10:15:22Z
dc.date.issued2018
dc.identifier.citationComputational Methods for Mathematics, Science and Engineeringes
dc.identifier.isbn978-84-697-7861-6es
dc.identifier.urihttp://uvadoc.uva.es/handle/10324/32186
dc.description.abstractThe task consisting on estimating the translation, rotation and scaling of an image with respect to another take of the same scene obtained at different times, viewpoints and/or lightning conditions is known as image registration. Applications like environmental disasters management or rescue operations depend on real-time hyperspectral images registration, but most of the current FFT-based techniques ignore such performance needs. Ordóñez et al. proposed HYFMGPU [1], a single-GPU algorithm whose performance makes it suitable for real-time use cases. As hyperspectral sensors improve, both the size of images and the wavelength ranges covered are expected to increase, so that a multi-GPU implementation is proposed to satisfy such growing needs.es
dc.format.mimetypeapplication/pdfes
dc.language.isospaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.subjectInformáticaes
dc.titleTowards a multi-device versión of the HYFMGPU Algorithmes
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dc.title.eventCMMSE 2018es
dc.description.projectUniversidad de Valladolid (Consejería de Educación of Junta de Castilla y León, Ministerio de Economía, Industria y Competitividad of Spain, and European Regional Development Fund (ERDF) program: Project PCAS (TIN2017-88614-R), Project PROPHET (VA082P17) and CAPAP-H6 network (TIN2016-81840-REDT).es


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