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dc.contributor.authorDigne, Julie
dc.contributor.authorDimiccoli, Mariella
dc.contributor.authorSalembier Clairon, Philippe Jean
dc.contributor.authorSabater, Neus
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2013-11-06T13:51:09Z
dc.date.created2011
dc.date.issued2011
dc.identifier.citationDigne, J. [et al.]. Neighborhood filters and the recovery of 3D information. A: "Handbook of mathematical methods in imaging". 2011, p. 1203-1229.
dc.identifier.isbn978-0-387-92919-4
dc.identifier.urihttp://hdl.handle.net/2117/20541
dc.description.abstractFollowing their success in image processing (see Chap. 26), neighborhood filters have been extended to 3D surface processing. This adaptation is not straightforward. It has led to several variants for surfaces depending on whether the surface is defined as a mesh, or as a raw data point set. The image gray level in the bilateral similarity measure is replaced by a geometric information such as the normal or the curvature. The first section of this chapter reviews the variants of 3D mesh bilateral filters and compares them to the simplest possible isotropic filter, the mean curvature motion. In a second part, this chapter reviews applications of the bilateral filter to a data composed of a sparse depth map (or of depth cues) and of the image on which they have been computed. Such sparse depth cues can be obtained by stereo vision or by psychophysical techniques. The underlying assumption to these applications is that pixels with similar intensity around a region are likely to have similar depths. Therefore, when diffusing depth information with a bilateral filter based on locality and color similarity, the discontinuities in depth are assured to be consistent with the color discontinuities, which is generally a desirable property. In the reviewed applications, this ends up with the reconstruction of a dense perceptual depth map from the joint data of an image and of depth cues.
dc.format.extent27 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística
dc.subject.lcshImaging systems
dc.subject.lcshMathematics
dc.titleNeighborhood filters and the recovery of 3D information
dc.typePart of book or chapter of book
dc.subject.lemacTècniques d'imatge
dc.subject.lemacMatemàtica
dc.contributor.groupUniversitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.identifier.doi10.1007/978-0-387-92920-0_27
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://link.springer.com/referenceworkentry/10.1007%2F978-0-387-92920-0_27
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac12868543
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorDigne, J.; Dimiccoli, M.; Salembier, P.; Sabater, N.
local.citation.publicationNameHandbook of mathematical methods in imaging
local.citation.startingPage1203
local.citation.endingPage1229


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Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain