Constant flow sampling: a method to automatically select the regularization parameter in image registration

Authors:
Benoît Compte;Adrien Bartoli;Daniel Pizarro
Affiliations:
ISIT, Université d'Auvergne, Clermont-Ferrand, France;ISIT, Université d'Auvergne, Clermont-Ferrand, France;University of Alcala, Alcala de Henares, Spain
Venue:
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Year:
2012

Citing 3
Cited 0

Maximizing the Predictivity of Smooth Deformable Image Warps through Cross-Validation

Journal of Mathematical Imaging and Vision
Efficient Camera Smoothing in Sequential Structure-from-Motion Using Approximate Cross-Validation

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
On Total Variation Minimization and Surface Evolution Using Parametric Maximum Flows

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a method to automatically select the regularization parameter in the two-term compound cost function used in image registration. Our method is called CFS (Constant Flow Sampling). It samples the regularization parameter using the constraint that the warp-induced image flow be of constant magnitude on average. Compared to other methods, CFS provably provides a global solution at a specified precision and within a finite number of steps. CFS can be embedded within any algorithm minimizing a two-term compound cost function depending on a regularization parameter. We report experimental results on the registration of several datasets of laparoscopic images.