by Attila Tanacs, Joakim Lindblad, Natasa Sladoje, Zoltan Kato
Abstract:
Registration is a fundamental task in image processing, it is used to determine geometric correspondences between images taken at different times and/or from different viewpoints. Here we propose a general framework in $n$-dimensions to solve binary shape/object matching problems without the need of establishing additional point or other type of correspondences. The approach is based on generating and solving polynomial systems of equations. We also propose an extension which, provided that a suitable segmentation method can produce a fuzzy border representation, further increases the registration precision. Via numerous synthetic and real test we examine the different solution techniques of the polynomial systems of equations. We take into account a direct analytical, an iterative least-squares, and a combined method. Iterative and combined approaches produce the most precise results. Comparison is made against competing methods for rigid-body problems. Our method is orders of magnitude faster and is able to recover alignment regardless of the magnitude of the deformation compared to the narrow capture range of others. The applicability of the proposed methods is demonstrated on real X-ray images of hip replacement implants and 3D CT volumes of the pelvic area. Since the images must be parsed through only once, our approach is especially suitable for solving registration problems of large images.
Reference:
Attila Tanacs, Joakim Lindblad, Natasa Sladoje, Zoltan Kato, Estimation of Linear Deformations of 2D and 3D Fuzzy Objects, In Pattern Recognition, volume 48, no. 4, pp. 1391-1403, 2015, Elsevier.
Bibtex Entry:
@string{pattrec="Pattern Recognition"}
@string{elsevier="Elsevier"}
@ARTICLE{Tanacs-etal2015,
author = {Attila Tanacs and Joakim Lindblad and Natasa Sladoje
and Zoltan Kato},
title = {Estimation of Linear Deformations of {2D} and {3D}
Fuzzy Objects},
journal = pattrec,
year = 2015,
publisher = elsevier,
volume = {48},
number = {4},
doi = {j.patcog.2014.10.006},
pages = {1391--1403},
abstract = {Registration is a fundamental task in image
processing, it is used to determine geometric
correspondences between images taken at different
times and/or from different viewpoints. Here we
propose a general framework in $n$-dimensions to
solve binary shape/object matching problems without
the need of establishing additional point or other
type of correspondences. The approach is based on
generating and solving polynomial systems of
equations. We also propose an extension which,
provided that a suitable segmentation method can
produce a fuzzy border representation, further
increases the registration precision. Via numerous
synthetic and real test we examine the different
solution techniques of the polynomial systems of
equations. We take into account a direct analytical,
an iterative least-squares, and a combined method.
Iterative and combined approaches produce the most
precise results. Comparison is made against
competing methods for rigid-body problems. Our
method is orders of magnitude faster and is able to
recover alignment regardless of the magnitude of the
deformation compared to the narrow capture range of
others. The applicability of the proposed methods is
demonstrated on real X-ray images of hip replacement
implants and 3D CT volumes of the pelvic area. Since
the images must be parsed through only once, our
approach is especially suitable for solving
registration problems of large images.},
month = apr
}