Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/11280
Campo DC Valoridioma
dc.contributor.authorMeinhardt-Llopis, Enricen_US
dc.contributor.authorSánchez, Javieren_US
dc.date.accessioned2014-02-03T11:54:24Z-
dc.date.accessioned2018-03-15T14:26:41Z-
dc.date.available2014-02-03T11:54:24Z-
dc.date.available2018-03-15T14:26:41Z-
dc.date.issued2013en_US
dc.identifier.issn2105-1232en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/11280-
dc.description.abstractThe seminal work of Horn and Schunck [8] is the first variational method for optical flow estimation. It introduced a novel framework where the optical flow is computed as the solution of a minimization problem. From the assumption that pixel intensities do not change over time, the optical flow constraint equation is derived. This equation relates the optical flow with the derivatives of the image. There are infinitely many vector fields that satisfy the optical flow constraint, thus the problem is ill-posed. To overcome this problem, Horn and Schunck introduced an additional regularity condition that restricts the possible solutions. Their method minimizes both the optical flow constraint and the magnitude of the variations of the flow field, producing smooth vector fields. One of the limitations of this method is that, typically, it can only estimate small motions. In the presence of large displacements, this method fails when the gradient of the image is not smooth enough. In this work, we describe an implementation of the original Horn and Schunck method and also introduce a multi-scale strategy in order to deal with larger displacements. For this multi-scale strategy, we create a pyramidal structure of downsampled images and change the optical flow constraint equation with a nonlinear formulation. In order to tackle this nonlinear formula, we linearize it and solve the method iteratively in each scale. In this sense, there are two common approaches: one that computes the motion increment in the iterations, like in ; or the one we follow, that computes the full flow during the iterations, like in. The solutions are incrementally refined ower the scales. This pyramidal structure is a standard tool in many optical flow methods.en_US
dc.languageengen_US
dc.relation.ispartofImage Processing On Lineen_US
dc.rightsby-nc-nd-
dc.sourceImage Processing On Line [ISSN 2105-1232], v. 3, p. 151-172en_US
dc.subject220990 Tratamiento digital. Imágenesen_US
dc.subject.otherOptical flowen_US
dc.subject.otherEstimationen_US
dc.titleHorn-schunck optical flow with a multi-scale strategyen_US
dc.typeArticleen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.identifier.doi10.5201/ipol.2013.20en_US
dc.identifier.isi000218821000006-
dc.identifier.absysnet676620-
dc.description.lastpage172en_US
dc.description.firstpage151en_US
dc.relation.volume3en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess-
dc.type2Artículoen_US
dc.contributor.daisngid2751289-
dc.contributor.daisngid1335721-
dc.contributor.daisngid1561914-
dc.description.numberofpages22en_US
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Meinhardt-Llopis, E-
dc.contributor.wosstandardWOS:Sanchez, J-
dc.contributor.wosstandardWOS:Kondermann, D-
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INFen_US
dc.description.esciESCI
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptGIR IUCES: Centro de Tecnologías de la Imagen-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0001-8514-4350-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.fullNameSánchez Pérez, Javier-
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