by Zsolt Santa, Zoltan Kato
Abstract:
A novel correspondence-less approach is proposed to find a non-linear aligning transformation between a pair of deformable 3D objects. Herein, we consider a polynomial deformation model, but our framework can be easily adapted to other common deformations. The basic idea of the proposed method is to set up a system of nonlinear equations whose solution directly provides the parameters of the aligning transformation. Each equation is generated by integrating a nonlinear function over the object’s domains. Thus the number of equations is determined by the number of adopted nonlinear functions yielding a flexible mechanism to generate sufficiently many equations. While classical approaches would establish correspondences between the shapes, our method works without landmarks. The efficiency of the proposed approach has been demonstrated on a large synthetic dataset as well as in the context of medical image registration.
Reference:
Zsolt Santa, Zoltan Kato, Elastic Registration of 3D Deformable Objects, In Proceedings of International Conference on Digital Image Computing: Techniques and Applications, Fremantle, Australia, pp. 1-7, 2012, IEEE.
Bibtex Entry:
@string{dicta="Proceedings of International Conference on Digital Image Computing: Techniques and Applications"}
@INPROCEEDINGS{Santa-Kato2012,
author = {Zsolt Santa and Zoltan Kato},
title = {Elastic Registration of 3{D} Deformable Objects},
booktitle = dicta,
year = {2012},
pages = {1--7},
address = {Fremantle, Australia},
month = dec,
publisher = {IEEE},
doi = {10.1109/DICTA.2012.6411674},
abstract = {A novel correspondence-less approach is proposed to find a non-linear
aligning transformation between a pair of deformable 3D objects.
Herein, we consider a polynomial deformation model, but our framework
can be easily adapted to other common deformations. The basic idea
of the proposed method is to set up a system of nonlinear equations
whose solution directly provides the parameters of the aligning transformation.
Each equation is generated by integrating a nonlinear function over
the object’s domains. Thus the number of equations is determined
by the number of adopted nonlinear functions yielding a flexible
mechanism to generate sufficiently many equations. While classical
approaches would establish correspondences between the shapes, our
method works without landmarks. The efficiency of the proposed approach
has been demonstrated on a large synthetic dataset as well as in
the context of medical image registration.}
}