GA–SSD–ARC–NLM for parametric image registration

Authors:
Felix Calderon;Leonardo Romero;Juan Flores
Affiliations:
División de Estudios de Posgrado. Facultad de Ingeniería Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, México;División de Estudios de Posgrado. Facultad de Ingeniería Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, México;División de Estudios de Posgrado. Facultad de Ingeniería Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, México
Venue:
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2006

Citing 7
Cited 1

Computer graphics (2nd ed.): C version

Computer graphics (2nd ed.): C version
On the unification of line processes, outlier rejection, and robust statistics with applications in early vision

International Journal of Computer Vision
Practical genetic algorithms

Practical genetic algorithms
3d Computer Graphics with Cdrom

3d Computer Graphics with Cdrom
Multiple View Geometry in Computer Vision

Multiple View Geometry in Computer Vision
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)

Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Video Mosaics for Virtual Environments

IEEE Computer Graphics and Applications

Surface-normal estimation with neighborhood reorganization for 3D reconstruction

CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present the GA–SSD–ARC–NLM, a new robust parametric image registration technique based on the non–parametric image registration SSD–ARC algorithm. This new algorithm minimizes a new cost function quite different to the original non-parametric SSD-ARC, which explicitly models outlier punishments, using a combination of a genetic algorithm and the Newton–Levenberg–Marquardt method. The performance of the new method was compared against two robust registration techniques: the Lorentzian Estimator and the RANSAC method. Experimental tests using gray level images with outliers (noise) were done using the three algorithms. The goal was to find an affine transformation to match two images; the new method improves the other methods when noisy images are used.