Optimization over random and gradient probabilistic pixel sampling for fast, robust multi-resolution image registration

Authors:
Boris N. Oreshkin;Tal Arbel
Affiliations:
Center of Intelligent Machines, McGill University, Montreal, Quebec, Canada;Center of Intelligent Machines, McGill University, Montreal, Quebec, Canada
Venue:
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Year:
2012

Citing 4
Cited 0

Alignment by Maximization of Mutual Information

International Journal of Computer Vision
Selection of observations in magnetic resonance spectroscopic imaging

ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
The importance of scale when selecting pixels for image registration

CRV '07 Proceedings of the Fourth Canadian Conference on Computer and Robot Vision
Efficient and reliable methods for direct parameterized image registration

Efficient and reliable methods for direct parameterized image registration

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an approach to fast image registration through probabilistic pixel sampling. We propose a practical scheme to leverage the benefits of two state-of-the-art pixel sampling approaches: gradient magnitude based pixel sampling and uniformly random sampling. Our framework involves learning the optimal balance between the two sampling schemes off-line during training, based on a small training dataset, using particle swarm optimization. We then test the proposed sampling approach on 3D rigid registration against two state-of-the-art approaches based on the popular, publicly available, Vanderbilt RIRE dataset. Our results indicate that the proposed sampling approach yields much faster, accurate and robust registration results when compared against the state-of-the-art.