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A variational approach to multi-modal image matching

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A variational approach to multi-modal image matching
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  A Variational Approach to Multi-Modal Image Matching Gerardo Hermosillo, Christophe Chefd’Hotel, Olivier Faugeras To cite this version: Gerardo Hermosillo, Christophe Chefd’Hotel, Olivier Faugeras. A Variational Approach toMulti-Modal Image Matching. RR-4117, 2001.  < inria-00072513 > HAL Id: inria-00072513https://hal.inria.fr/inria-00072513 Submitted on 24 May 2006 HAL  is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire  HAL , estdestin´ee au d´epˆot et `a la diffusion de documentsscientifiques de niveau recherche, publi´es ou non,´emanant des ´etablissements d’enseignement et derecherche fran¸cais ou ´etrangers, des laboratoirespublics ou priv´es.     I   S   S   N    0   2   4   9  -   6   3   9   9   I   S   R   N    I   N   R   I   A   /   R   R  -  -   4   1   1   7  -  -   F   R  +   E   N   G  apport  de recherche TH`EME 3 INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE  A Variational Approach to Multi-Modal Image Matching Gerardo Hermosillo Christophe Chefd’Hotel and Olivier Faugeras N° 4117 February 16, 2001  Unit´e de recherche INRIA Sophia Antipolis2004, route des Lucioles, B.P. 93, 06902 Sophia Antipolis Cedex (France) T´el´ephone : 04 92 38 77 77 - International : +33 4 92 38 77 77 — Fax : 04 92 38 77 65 - International : +33 4 92 38 77 65 A Variational Approach to Multi-Modal ImageMatching Gerardo Hermosillo Christophe Chefd’Hotel and Olivier Faugeras Th`eme 3 — Interaction homme-machine,images, donn´ees, connaissancesProjet RobotvisRapport de recherche n° 4117 — February 16, 2001 — 34 pages Abstract:  We address the problem of non-parametric multi-modal image match-ing. We propose a generic framework which relies on a global variational formula-tion and show its versatility through three different multi-modal registration meth-ods : supervised registration by joint intensity learning, maximization of the mutualinformation and maximization of the correlation ratio. Regularization is performedby using a functional borrowed from linear elasticity theory. We also consider ageometry-driven regularization method. Experiments on synthetic images and pre-liminary results on the realignment of MRI datasets are presented. Key-words:  Multi-modal Image Matching, Variational Methods, Registration,Optical Flow, Mutual Information, Correlation Ratio. A short version of this report has been submitted to the IEEE Workshop on Variational andLevel Set Methods in Computer Vision (VLSM01).This work was partially supported by EC contract No QLG3-CT-2000-30161,Mapawamo.  Une Approche Variationnelle pour l’AppariementMultimodal d’Images R´esum´e :  Nous consid´erons le probl`eme du recalage non-rigide entre images demodalit´es diff´erentes. Nous proposons un cadre g´en´eral qui repose sur une for-mulation variationnelle, que nous appliquons sous la forme de trois algorithmes derecalage multimodal: recalage supervis´e par apprentissage de la loi jointe, maximi-sation de l’information mutuelle, et maximisation du rapport de corr´elation. Pourpermettre une r´egularisation de la solution, nous utilisons un op´erateur issu dela th´eorie de l’´elasticit´e. Nous consid´erons aussi une m´ethode de r´egularisationavec pr´eservation des contours. Des r´esultats exp´erimentaux pr´eliminaires sur desimages synth´etiques et des donn´ees IRM sont pr´esent´es. Mots-cl´es :  Mise en Correspondence Multimodale, M ´ ethodes Variationnelles, Flot Optique, Information Mutuelle, Rapport de Corr ´ elation.
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