@article {1223, title = {Nonlinear Shape Registration without Correspondences}, journal = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE}, volume = {34}, year = {2012}, note = {UT: 000301747400009doi: 10.1109/TPAMI.2011.200}, month = {2012}, pages = {943 - 958}, publisher = {IEEE}, type = {Journal article}, abstract = {

In this paper, we propose a novel framework to estimate the parameters of a diffeomorphism that aligns a known shape and its distorted observation. Classical registration methods first establish correspondences between the shapes and then compute the transformation parameters from these landmarks. Herein, we trace back the problem to the solution of a system of nonlinear equations which directly gives the parameters of the aligning transformation. The proposed method provides a generic framework to recover any diffeomorphic deformation without established correspondences. It is easy to implement, not sensitive to the strength of the deformation, and robust against segmentation errors. The method has been applied to several commonly used transformation models. The performance of the proposed framework has been demonstrated on large synthetic data sets as well as in the context of various applications.

}, isbn = {0162-8828}, doi = {10.1109/TPAMI.2011.200 }, url = {http://www.inf.u-szeged.hu/~kato/papers/TPAMI-2010-03-0146.R2_Kato.pdf}, author = {Csaba Domokos and Jozsef Nemeth and Zoltan Kato} } @inbook {1219, title = {A Multi-Layer {\textquoteright}Gas of Circles{\textquoteright} Markov Random Field Model for the Extraction of Overlapping Near-Circular Objects}, booktitle = {Advances Concepts for Intelligent Vision Systems (ACIVS)}, series = {Lecture Notes in Computer Science}, number = {6915}, year = {2011}, note = {UT: 000306962700016}, month = {Aug 2011}, pages = {171 - 182}, publisher = {Springer-Verlag}, organization = {Springer-Verlag}, type = {Conference paper}, address = {Ghent, Belgium}, abstract = {

We propose a multi-layer binary Markov random field (MRF) model that assigns high probability to object configurations in the image domain consisting of an unknown number of possibly touching or overlapping near-circular objects of approximately a given size. Each layer has an associated binary field that specifies a region corresponding to objects. Overlapping objects are represented by regions in different layers. Within each layer, long-range interactions favor connected components of approximately circular shape, while regions in different layers that overlap are penalized. Used as a prior coupled with a suitable data likelihood, the model can be used for object extraction from images, e.g. cells in biological images or densely-packed tree crowns in remote sensing images. We present a theoretical and experimental analysis of the model, and demonstrate its performance on various synthetic and biomedical images.

}, isbn = {978-3-642-23686-0}, doi = {10.1007/978-3-642-23687-7_16}, url = {http://www.inf.u-szeged.hu/ipcg/publications/Year/2011.complete.xml$\#$Nemeth-etal2011} } @article {1286, title = {Nonlinear Shape Registration without Correspondences}, year = {2011}, month = {2011///}, type = {Software}, abstract = {

This is the sample implementation and benchmark dataset of the nonlinear registration of 2D shapes described in the following papers: Csaba Domokos, Jozsef Nemeth, and Zoltan Kato. Nonlinear Shape Registration without Correspondences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(5):943--958, May 2012. Note that the current demo program implements only planar homography deformations. Other deformations can be easily implemented based on the demo code.

}, url = {http://www.inf.u-szeged.hu/~kato/software/planarhombinregdemo.html}, author = {Zolt{\'a}n Korn{\'e}l T{\"o}r{\"o}k and Csaba Domokos and Jozsef Nemeth and Zoltan Kato} }