Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

Détails

ID Serval
serval:BIB_C1EB858C504A
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.
Périodique
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Auteur⸱e⸱s
Zosso D., Bresson X., Thiran J.P.
ISSN
1941-0042 (Electronic)
ISSN-L
1057-7149
Statut éditorial
Publié
Date de publication
02/2014
Peer-reviewed
Oui
Volume
23
Numéro
2
Pages
673-683
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Résumé
In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.

Pubmed
Web of science
Création de la notice
17/12/2013 17:37
Dernière modification de la notice
20/08/2019 15:36
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