@article {2104, title = {Estimation of linear deformations of 2D and 3D fuzzy objects}, journal = {PATTERN RECOGNITION}, volume = {48}, year = {2015}, month = {Apr 2015}, pages = {1387-1399}, publisher = {Elsevier}, type = {Journal Article}, 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.

}, doi = {10.1016/j.patcog.2014.10.006}, author = {Attila Tanacs and Joakim Lindbald and Nata{\v s}a Sladoje and Zoltan Kato} } @conference {1389, title = {2D {\'e}s 3D bin{\'a}ris objektumok line{\'a}ris deform{\'a}ci{\'o}-becsl{\'e}s{\'e}nek numerikus megold{\'a}si lehet{\H o}s{\'e}gei}, booktitle = {A K{\'e}pfeldolgoz{\'o}k {\'e}s Alakfelismer{\H o}k T{\'a}rsas{\'a}g{\'a}nak konferenci{\'a}ja - K{\'E}PAF 2013}, year = {2013}, month = {Jan 2013}, pages = {526 - 541}, publisher = {NJSZT-K{\'E}PAF}, organization = {NJSZT-K{\'E}PAF}, type = {Conference paper}, address = {Veszpr{\'e}m}, author = {Attila Tanacs and Joakim Lindblad and Nata{\v s}a Sladoje and Zoltan Kato}, editor = {L{\'a}szl{\'o} Cz{\'u}ni} } @conference {1222, title = {3D objektumok line{\'a}ris deform{\'a}ci{\'o}inak becsl{\'e}se}, booktitle = {A K{\'e}pfeldolgoz{\'o}k {\'e}s Alakfelismer{\H o}k T{\'a}rsas{\'a}g{\'a}nak konferenci{\'a}ja - K{\'E}PAF 2011}, year = {2011}, month = {Jan 2011}, pages = {471 - 480}, publisher = {NJSZT}, organization = {NJSZT}, type = {Conference paper}, address = {Szeged}, author = {Attila Tanacs and Joakim Lindblad and Nata{\v s}a Sladoje and Zoltan Kato}, editor = {Zoltan Kato and K{\'a}lm{\'a}n Pal{\'a}gyi} } @inbook {1224, title = {Estimation of linear deformations of 3D objects}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, year = {2010}, note = {UT: 000287728000038ScopusID: 78651064516doi: 10.1109/ICIP.2010.5650932}, month = {Sep 2010}, pages = {153 - 156}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Hong Kong, Hong Kong}, abstract = {

We propose a registration method to find affine transformations between 3D objects by constructing and solving an overdetermined system of polynomial equations. We utilize voxel coverage information for more precise object boundary description. An iterative solution enables us to easily adjust the method to recover e.g. rigid-body and similarity transformations. Synthetic tests show the advantage of the voxel coverage representation, and reveal the robustness properties of our method against different types of segmentation errors. The method is tested on a real medical CT volume. {\textcopyright} 2010 IEEE.

}, author = {Attila Tanacs and Joakim Lindblad and Nata{\v s}a Sladoje and Zoltan Kato} } @inbook {1225, title = {Recovering affine deformations of fuzzy shapes}, booktitle = {Image Analysis}, series = {Lecture Notes in Computer Science}, number = {5575}, year = {2009}, note = {UT: 000268661000075ScopusID: 70350676212doi: 10.1007/978-3-642-02230-2_75}, month = {June 2009}, pages = {735 - 744}, publisher = {Springer-Verlag}, organization = {Springer-Verlag}, type = {Conference paper}, address = {Oslo, Norway}, abstract = {

Fuzzy sets and fuzzy techniques are attracting increasing attention nowadays in the field of image processing and analysis. It has been shown that the information preserved by using fuzzy representation based on area coverage may be successfully utilized to improve precision and accuracy of several shape descriptors; geometric moments of a shape are among them. We propose to extend an existing binary shape matching method to take advantage of fuzzy object representation. The result of a synthetic test show that fuzzy representation yields smaller registration errors in average. A segmentation method is also presented to generate fuzzy segmentations of real images. The applicability of the proposed methods is demonstrated on real X-ray images of hip replacement implants. {\textcopyright} 2009 Springer Berlin Heidelberg.

}, doi = {10.1007/978-3-642-02230-2_75}, author = {Attila Tanacs and Csaba Domokos and Nata{\v s}a Sladoje and Joakim Lindblad and Zoltan Kato}, editor = {Arnt-Borre Salberg and Jon Yngve Hardeberg and Robert Jenssen} }