Show simple item record

dc.contributor.authorCohen, Laurent D.
HAL ID: 738939
dc.contributor.authorLefébure, Martin
dc.date.accessioned2011-05-10T12:50:29Z
dc.date.available2011-05-10T12:50:29Z
dc.date.issued2001
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6252
dc.language.isoenen
dc.subjectMotion estimationen
dc.subjectRegistrationen
dc.subjectOptical flowen
dc.subjectMulti-scaleen
dc.subjectMotion constraint equationen
dc.subjectGlobal minimizationen
dc.subjectStereo matchingen
dc.subject.ddc519en
dc.titleImage Registration, Optical Flow and Local Rigidityen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe address the theoretical problems of optical flow estimation and image registration in a multi-scale framework in any dimension. Much work has been done based on the minimization of a distance between a first image and a second image after applying deformation or motion field. Usually no justification is given about convergence of the algorithm used. We start by showing, in the translation case, that convergence to the global minimum is made easier by applying a low pass filter to the images hence making the energy ldquoconvex enoughrdquo. In order to keep convergence to the global minimum in the general case, we introduce a local rigidity hypothesis on the unknown deformation. We then deduce a new natural motion constraint equation (MCE) at each scale using the Dirichlet low pass operator. This transforms the problem to solving the energy minimization in a finite dimensional subspace of approximation obtained through Fourier Decomposition. This allows us to derive sufficient conditions for convergence of a new multi-scale and iterative motion estimation/registration scheme towards a global minimum of the usual nonlinear energy instead of a local minimum as did all previous methods. Although some of the sufficient conditions cannot always be fulfilled because of the absence of the necessary a priori knowledge on the motion, we use an implicit approach. We illustrate our method by showing results on synthetic and real examples in dimension 1 (signal matching, Stereo) and 2 (Motion, Registration, Morphing), including large deformation experiments.en
dc.relation.isversionofjnlnameJournal of Mathematical Imaging and Vision
dc.relation.isversionofjnlvol14en
dc.relation.isversionofjnlissue2en
dc.relation.isversionofjnldate2001
dc.relation.isversionofjnlpages131-147en
dc.relation.isversionofdoihttp://dx.doi.org/10.1023/A:1011259231755en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherSpringeren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record