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.

This paper addresses the problem of simultaneous estimation of different linear deformations, resulting in a global non-linear transformation, between an original object and its broken fragments. A general framework is proposed without using correspondences, where the solution of a polynomial system of equations directly provides the parameters of the alignment. We quantitatively evaluate the proposed algorithm on a large synthetic dataset containing 2D and 3D images, where linear (rigid-body and affine) transformations are considered. We also conduct an exhaustive analysis of the robustness against segmentation errors and the numerical stability of the proposed method. Moreover, we present experiments on 2D real images as well as on volumetric medical images.

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}, issn = {0162-8828 }, doi = {10.1109/TPAMI.2015.2450726 }, author = {Csaba Domokos and Zoltan Kato} } @conference {2102, title = {3D Reconstruction of Planar Patches Seen by Omnidirectional Cameras}, booktitle = {International Conference on Digital Image Computing: Techniques and Applications (DICTA)}, year = {2014}, pages = {1-8}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Wollongong, Australia}, author = {Jozsef Molnar and Robert Frohlich and Chetverikov Dmitrij and Zoltan Kato}, editor = {Abdesselam Bouzerdoum and Lei Wang and Philip Ogunbona and Wanqing Li and Son Lam Phung} } @inbook {2101, title = {3D Reconstruction of Planar Surface Patches: A Direct Solution}, booktitle = {Proceedings of the ACCV Workshop on Big Data in 3D Computer Vision (ACCV-BigData3DCV)}, year = {2014}, month = {Nov 2014}, pages = {1-8.}, publisher = {Springer}, organization = {Springer}, type = {Conference paper}, address = {Singapore, Szingap{\'u}r}, author = {Jozsef Molnar and Rui Huang and Zoltan Kato}, editor = {Jian Zhang and Mohammed Bennamoun and Fatih Porikli} } @conference {2100, title = {Affine Alignment of Occluded Shapes}, booktitle = {International Conference on Pattern Recognition (ICPR)}, year = {2014}, month = {Aug 2014}, pages = {2155-2160}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Stockholm, Sv{\'e}dorsz{\'a}g}, isbn = {978-4-9906441-0-9}, author = {Zsolt Santa and Zoltan Kato}, editor = {Michael Felsberg} } @conference {2103, title = {Establishing Correspondences between Planar Image Patches}, booktitle = {International Conference on Digital Image Computing: Techniques and Applications (DICTA)}, year = {2014}, month = {2014}, pages = {1-7}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Wollongong, Australia}, author = {Attila Tanacs and Andr{\'a}s Majdik and Jozsef Molnar and Atul Rai and Zoltan Kato}, editor = {Abdesselam Bouzerdoum and Lei Wang and Philip Ogunbona and Wanqing Li and Son Lam Phung} } @inbook {1990, title = {K{\'e}pfeldolgoz{\'a}s a szegedi informatikus-k{\'e}pz{\'e}sben}, booktitle = {Informatika a fels{\H o}oktat{\'a}sban 2014}, year = {2014}, month = {2014}, pages = {667-675}, publisher = {University of Debrecen}, organization = {University of Debrecen}, type = {Conference paper}, address = {Debrecen, Hungary}, issn = {978-963-473-712-4}, author = {P{\'e}ter Bal{\'a}zs and Endre Katona and Zoltan Kato and Antal Nagy and G{\'a}bor N{\'e}meth and L{\'a}szl{\'o} G{\'a}bor Ny{\'u}l and K{\'a}lm{\'a}n Pal{\'a}gyi and Attila Tanacs and L{\'a}szl{\'o} G{\'a}bor Varga}, editor = {Roland Kunkli and Ildik{\'o} Papp and Ed{\'e}n{\'e} Rutkovszky} } @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 {1299, title = {Correspondence-less non-rigid registration of triangular surface meshes}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2013}, note = {ScopusID: 84887348013doi: 10.1109/CVPR.2013.295}, month = {June 2013}, pages = {2275 - 2282}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Portland, OR, USA}, abstract = {A novel correspondence-less approach is proposed to find a thin plate spline map between a pair of deformable 3D objects represented by triangular surface meshes. The proposed method works without landmark extraction and feature correspondences. The aligning transformation is found simply by solving a system of nonlinear equations. Each equation is generated by integrating a nonlinear function over the object{\textquoteright}s domains. We derive recursive formulas for the efficient computation of these integrals. Based on a series of comparative tests on a large synthetic dataset, our triangular mesh-based algorithm outperforms state of the art methods both in terms of computing time and accuracy. The applicability of the proposed approach has been demonstrated on the registration of 3D lung CT volumes. {\textcopyright} 2013 IEEE.

}, doi = {10.1109/CVPR.2013.295}, author = {Zsolt Santa and Zoltan Kato} } @inbook {1288, title = {Elastic Registration of 3D Deformable Objects}, booktitle = {Proceedings of International Conference on Digital Image Computing: Techniques and Applications (DICTA)}, year = {2013}, note = {UT: 000316318400010doi: 10.1109/DICTA.2012.6411674}, month = {Nov 2013}, pages = {1 - 7}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {New York}, 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{\textquoteright}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.

}, doi = {10.1109/DICTA.2012.6411674}, url = {http://www.inf.u-szeged.hu/~kato/papers/dicta2012.pdf}, author = {Zsolt Santa and Zoltan Kato}, editor = {Geoff West and P{\'e}ter K{\"o}vesi} } @inbook {1298, title = {Evaluation of Point Matching Methods for Wide-baseline Stereo Correspondence on Mobile Platforms}, booktitle = {Proceedings of the International Symposium on Image and Signal Processing and Analysis (ISPA)}, year = {2013}, month = {Sep 2013}, pages = {806 - 811}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Trieste}, abstract = {Wide-baseline stereo matching is a common problem of computer vision. By the explosion of smartphones equipped with camera modules, many classical computer vision solutions have been adapted to such platforms. Considering the widespread use of various networking options for mobile phones, one can consider a set of smart phones as an ad-hoc camera network, where each camera is equipped with a more and more powerful computing engine in addition to a limited bandwidth communication with other devices. Therefore the performance of classical vision algorithms in a collaborative mobile environment is of particular interest. In such a scenario we expect that the images are taken almost simultaneously but from different viewpoints, implying that the camera poses are significantly different but lighting conditions are the same. In this work, we provide quantitative comparison of the most important keypoint detectors and descriptors in the context of wide baseline stereo matching. We found that for resolution of 2 megapixels images the current mobile hardware is capable of providing results efficiently.

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}, doi = {10.1109/ISPA.2013.6703848}, author = {Endre Juh{\'a}sz and Attila Tanacs and Zoltan Kato}, editor = {Giovanni Ramponi and Sven Lon{\v c}ari{\'c} and Alberto Carini and Karen Egiazarian} } @inbook {1290, title = {Linear and nonlinear shape alignment without correspondences}, booktitle = {Proceedings of International Joint Conference on Computer Vision, Imaging and Computer Graphics - Theory and Applications (Revised Selected Papers)}, series = {Communications in Computer and Information Science}, number = {359}, year = {2013}, month = {Feb 2013}, pages = {3 - 17}, publisher = {Springer Verlag}, organization = {Springer Verlag}, type = {Conference paper}, address = {Berlin; Heidelberg; New York; London; Paris; Tokyo}, abstract = {We consider the estimation of diffeomorphic deformations aligning a known binary shape and its distorted observation. The classical solution consists in extracting landmarks, establishing correspondences and then the aligning transformation is obtained via a complex optimization procedure. Herein we present an alternative solution which works without landmark correspondences, is independent of the magnitude of transformation, easy to implement, and has a linear time complexity. The proposed universal framework is capable of recovering linear as well as nonlinear deformations.

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}, doi = {10.1007/978-3-642-38241-3_1}, url = {http://www.inf.u-szeged.hu/~kato/papers/visapp2012.pdf}, author = {Zoltan Kato}, editor = {Paul Richard and Gabriela Csurka} } @inbook {1301, title = {Pose Estimation of Ad-hoc Mobile Camera Networks}, booktitle = {International Conference on Digital Image Computing: Techniques and Applications (DICTA)}, year = {2013}, month = {2013}, pages = {88 - 95}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Hobart, TAS }, abstract = {An algorithm is proposed for the pose estimation of ad-hoc mobile camera networks with overlapping views. The main challenge is to estimate camera parameters with respect to the 3D scene without any specific calibration pattern, hence allowing for a consistent, camera-independent world coordinate system. The only assumption about the scene is that it contains a planar surface patch of a low-rank texture, which is visible in at least two cameras. Such low-rank patterns are quite common in urban environments. The proposed algorithm consists of three main steps: relative pose estimation of the cameras within the network, followed by the localization of the network within the 3D scene using a low-rank surface patch, and finally the estimation of a consistent scale for the whole system. The algorithm follows a distributed architecture, hence the computing power of the participating mobile devices are efficiently used. The performance and robustness of the proposed algorithm have been analyzed on both synthetic and real data. Experimental results confirmed the relevance and applicability of the method.

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}, doi = {10.1109/DICTA.2013.6691514}, author = {Zsolt Santa and Zoltan Kato}, editor = {Paulo de Souza and Ulrich Engelke and Ashfaqur Rahman} } @inbook {1302, title = {Targetless Calibration of a Lidar - Perspective Camera Pair}, booktitle = {Proceedings of ICCV Workshop on Big Data in 3D Computer Vision}, year = {2013}, note = {doi: 10.1109/ICCVW.2013.92}, month = {Dec 2013}, pages = {668 - 675}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Sydney, NSW }, abstract = {A novel method is proposed for the calibration of a camera - 3D lidar pair without the use of any special calibration pattern or point correspondences. The proposed method has no specific assumption about the data source: plain depth information is expected from the lidar scan and a simple perspective camera is used for the 2D images. The calibration is solved as a 2D-3D registration problem using a minimum of one (for extrinsic) or two (for intrinsic-extrinsic) planar regions visible in both cameras. The registration is then traced back to the solution of a non-linear system of equations which directly provides the calibration parameters between the bases of the two sensors. The method has been tested on a large set of synthetic lidar-camera image pairs as well as on real data acquired in outdoor environment.

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}, doi = {10.1109/ICCVW.2013.92 }, author = {Tam{\'a}s Levente and Zoltan Kato}, editor = {Jian Zhang and Mohammed Bennamoun and Dan Schonfeld and Zhengyou Zhang} } @inbook {1300, title = {A unifying framework for correspondence-less shape alignment and its medical applications}, booktitle = { Intelligent Interactive Technologies and Multimedia }, series = {Communications in Computer and Information Science}, volume = {276 CCIS}, number = {276}, year = {2013}, note = {ScopusID: 84875170012doi: 10.1007/978-3-642-37463-0_4T3 2nd International Conference on Intelligent Interactive Technologies and Multimedia, IITM 2013Y2 9 March 2013 through 11 March 2013 CY Allahabad }, month = {March 2013}, pages = {40 - 52}, publisher = {Springer}, organization = {Springer}, type = {Conference paper}, address = {Allahabad, India}, abstract = {We give an overview of our general framework for registering 2D and 3D objects without correspondences. Classical solutions consist in extracting landmarks, establishing correspondences and then the aligning transformation is obtained via a complex optimization procedure. In contrast, our framework works without landmark correspondences, is independent of the magnitude of transformation, easy to implement, and has a linear time complexity. The efficiency and robustness of the method has been demonstarted using various deformations models. Herein, we will focus on medical applications. {\textcopyright} 2013 Springer-Verlag.

}, isbn = {1865-0929}, doi = {10.1007/978-3-642-37463-0_4}, author = {Zoltan Kato} } @article {1285, title = {Affine Registration of 3D Objects}, year = {2012}, month = {2012///}, type = {Software}, abstract = {This is the sample implementation and benchmark dataset of the binary image registration algorithm described in the following papers: Attila Tanacs and Zoltan Kato. Fast Linear Registration of 3D Objects Segmented from Medical Images. In Proceedings of International Conference on BioMedical Engineering and Informatics, Shanghai, China, pages 299--303, October 2011. IEEE. Attila Tanacs, Joakim Lindblad, Natasa Sladoje and Zoltan Kato. Estimation of Linear Deformations of 3D Objects. In Proceedings of International Conference on Image Processing, Hong Kong, China, pp. 153-156, September 2010. IEEE.

}, url = {http://www.inf.u-szeged.hu/~kato/software/affbin3dregdemo.html} } @book {1287, title = {Markov random fields in image segmentation}, year = {2012}, note = {doi: 10.1561/2000000035}, month = {2012}, publisher = {Now Publishers}, organization = {Now Publishers}, type = {Book}, address = {Hanover, NH}, abstract = {Markov Random Fields in Image Segmentation introduces the fundamentals of Markovian modeling in image segmentation as well as providing a brief overview of recent advances in the field.

}, author = {Zoltan Kato and Josiane Zerubia} } @conference {1251, title = {A Multi-Layer Phase Field Model for Extracting Multiple Near-Circular Objects}, booktitle = {International Conference on Pattern Recognition (ICPR)}, year = {2012}, month = {Nov 2012}, pages = {1427 - 1430}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Tsukuba, Japan}, abstract = {This paper proposes a functional that assigns low {\textquoteleft}energy{\textquoteright} to sets of subsets of the image domain consisting of a number of possibly overlapping near-circular regions of approximately a given radius: a {\textquoteleft}gas of circles{\textquoteright}. The model can be used as a prior for object extraction whenever the objects conform to the {\textquoteleft}gas of circles{\textquoteright} geometry, e.g. cells in biological images. Configurations are represented by a multi-layer phase field. Each layer has an associated function, regions being defined by thresholding. Intra-layer interactions assign low energy to configurations consisting of non-overlapping near-circular regions, while overlapping regions are represented in separate layers. Inter-layer interactions penalize overlaps. Here we present a theoretical and experimental analysis of the model.

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}, isbn = {978-1-4673-2216-4 }, author = {Csaba Molnar and Zoltan Kato and Ian Jermyn}, editor = {Jan-Olof Eklundh and Yuichi Ohta and Steven Tanimoto} } @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.

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}, 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 {1249, title = {Parametric Stochastic Modeling for Color Image Segmentation and Texture Characterization}, booktitle = {Advanced color image processing and analysis}, year = {2012}, month = {2012}, pages = {279 - 325}, publisher = {Springer}, organization = {Springer}, type = {Book chapter}, address = {Berlin; Heidelberg; New York; London; Paris; Tokyo}, abstract = {*Black should be made a color of light* Clemence Boulouque

Parametric stochastic models offer the definition of color and/or texture features based on model parameters, which is of interest for color texture classification, segmentation and synthesis.

In this chapter, distribution of colors in the images through various parametric approximations including multivariate Gaussian distribution, multivariate Gaussian mixture models (MGMM) and Wishart distribution, is discussed. In the context of Bayesian color image segmentation, various aspects of sampling from the posterior distributions to estimate the color distribution from MGMM and the label field, using different move types are also discussed. These include reversible jump mechanism from MCMC methodology. Experimental results on color images are presented and discussed.

Then, we give some materials for the description of color spatial structure using Markov Random Fields (MRF), and more particularly multichannel GMRF, and multichannel linear prediction models. In this last approach, two dimensional complex multichannel versions of both causal and non-causal models are discussed to perform the simultaneous parametric power spectrum estimation of the luminance and the chrominance channels of the color image. Application of these models to the classification and segmentation of color texture images is also illustrated.

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}, isbn = {978-1-4419-6189-1}, doi = {10.1007/978-1-4419-6190-7_9}, author = {Imtnan-Ul-Haque Qazi and Oliver Alata and Zoltan Kato}, editor = {Christine Fernandez-Maloigne} } @conference {1253, title = {Simultaneous Affine Registration of Multiple Shapes}, booktitle = {International Conference on Pattern Recognition (ICPR)}, year = {2012}, month = {Nov 2012}, pages = {9 - 12}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Tsukuba, Japan}, abstract = {The problem of simultaneously estimating affine deformations between multiple objects occur in many applications. Herein, a direct method is proposed which provides the result as a solution of a linear system of equations without establishing correspondences between the objects. The key idea is to construct enough linearly independent equations using covariant functions, and then finding the solution simultaneously for all affine transformations. Quantitative evaluation confirms the performance of the method.

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}, isbn = {978-1-4673-2216-4 }, author = {Csaba Domokos and Zoltan Kato}, editor = {Jan-Olof Eklundh and Yuichi Ohta and Steven Tanimoto} } @conference {1252, title = {Spectral clustering to model deformations for fast multimodal prostate registration}, booktitle = {International Conference on Pattern Recognition (ICPR)}, year = {2012}, month = {Nov 2012}, pages = {2622 - 2625}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Tsukuba, Japan}, abstract = {This paper proposes a method to learn deformation parameters off-line for fast multimodal registration of ultrasound and magnetic resonance prostate images during ultrasound guided needle biopsy. The registration method involves spectral clustering of the deformation parameters obtained from a spline-based nonlinear diffeomorphism between training magnetic resonance and ultrasound prostate images. The deformation models built from the principal eigen-modes of the clusters are then applied on a test magnetic resonance image to register with the test ultrasound prostate image. The deformation model with the least registration error is finally chosen as the optimal model for deformable registration. The rationale behind modeling deformations is to achieve fast multimodal registration of prostate images while maintaining registration accuracies which is otherwise computationally expensive. The method is validated for 25 patients each with a pair of corresponding magnetic resonance and ultrasound images in a leave-one-out validation framework. The average registration accuracies i.e. Dice similarity coefficient of 0.927 {\textpm} 0.025, 95\% Hausdorff distance of 5.14 {\textpm} 3.67 mm and target registration error of 2.44 {\textpm} 1.17 mm are obtained by our method with a speed-up in computation time by 98\% when compared to Mitra et al. [7].

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}, isbn = {978-1-4673-2216-4 }, url = {http://hal.archives-ouvertes.fr/docs/00/71/09/43/PDF/ICPR_Jhimli.pdf}, author = {Jhimli Mitra and Zoltan Kato and Soumya Ghose and Desire Sidibe and Robert Mart{\'\i} and Xavier Llad{\'o} and Oliver Arnau and Joan C Vilanova and Fabrice Meriaudeau}, editor = {Jan-Olof Eklundh and Yuichi Ohta and Steven Tanimoto} } @article {1248, title = {A spline-based non-linear diffeomorphism for multimodal prostate registration.}, journal = {MEDICAL IMAGE ANALYSIS}, volume = {16}, year = {2012}, note = {UT: 000309694100015ScopusID: 84866118888doi: 10.1016/j.media.2012.04.006}, month = {Aug 2012}, pages = {1259 - 1279}, type = {Journal article}, abstract = {This paper presents a novel method for non-rigid registration of transrectal ultrasound and magnetic resonance prostate images based on a non-linear regularized framework of point correspondences obtained from a statistical measure of shape-contexts. The segmented prostate shapes are represented by shape-contexts and the Bhattacharyya distance between the shape representations is used to find the point correspondences between the 2D fixed and moving images. The registration method involves parametric estimation of the non-linear diffeomorphism between the multimodal images and has its basis in solving a set of non-linear equations of thin-plate splines. The solution is obtained as the least-squares solution of an over-determined system of non-linear equations constructed by integrating a set of non-linear functions over the fixed and moving images. However, this may not result in clinically acceptable transformations of the anatomical targets. Therefore, the regularized bending energy of the thin-plate splines along with the localization error of established correspondences should be included in the system of equations. The registration accuracies of the proposed method are evaluated in 20 pairs of prostate mid-gland ultrasound and magnetic resonance images. The results obtained in terms of Dice similarity coefficient show an average of 0.980+/-0.004, average 95\% Hausdorff distance of 1.63+/-0.48mm and mean target registration and target localization errors of 1.60+/-1.17mm and 0.15+/-0.12mm respectively.

}, isbn = {1361-8415}, doi = {10.1016/j.media.2012.04.006}, author = {Jhimli Mitra and Zoltan Kato and Robert Mart{\'\i} and Oliver Arnau and Xavier Llad{\'o} and Desire Sidibe and Soumya Ghose and Joan C Vilanova and Josep Comet and Fabrice Meriaudeau} } @inbook {1254, title = {A Unifying Framework for Correspondence-less Linear Shape Alignment}, booktitle = {International Conference on Image Analysis and Recognition (ICIAR)}, series = {Lecture Notes in Computer Science}, number = {7324}, year = {2012}, note = {UT: 000323558000033}, month = {June 2012}, pages = {277 - 284}, publisher = {Springer Verlag}, organization = {Springer Verlag}, type = {Conference paper}, address = {Aveiro, Portugal}, abstract = {We consider the estimation of linear transformations aligning a known binary shape and its distorted observation. The classical way to solve this registration problem is to find correspondences between the two images and then compute the transformation parameters from these landmarks. Here we propose a unified framework where the exact transformation is obtained as the solution of either a polynomial or a linear system of equations without establishing correspondences. The advantages of the proposed solutions are that they are fast, easy to implement, have linear time complexity, work without landmark correspondences and are independent of the magnitude of transformation.

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}, isbn = {978-3-642-31294-6}, doi = {10.1007/978-3-642-31295-3_33}, author = {Zoltan Kato}, editor = {Aur{\'e}lio Campilho} } @conference {1289, title = {A Unifying Framework for Non-linear Registration of 3D Objects}, booktitle = {IEEE International Conference on Cognitive Infocommunications (CogInfoCom)}, year = {2012}, note = {UT: 000320454200086}, month = {Dec 2012}, pages = {547 - 552}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Kosice, Slovakia }, abstract = {An extension of our earlier work is proposed to find a non-linear aligning transformation between a pair of deformable 3D objects. The basic idea 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{\textquoteright}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. Experiments with 3D polynomial and thin plate spline deformations confirm the performance of the framework.

\

}, isbn = {978-1-4673-5187-4 }, doi = {10.1109/CogInfoCom.2012.6422041}, url = {http://www.inf.u-szeged.hu/~kato/papers/coginfocomm2012.pdf}, author = {Zsolt Santa and Zoltan Kato} } @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} } @conference {1220, title = {Affin Puzzle: Deform{\'a}lt objektumdarabok helyre{\'a}ll{\'\i}t{\'a}sa megfeleltet{\'e}sek n{\'e}lk{\"u}l}, 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}, note = {Kuba Attila D{\'\i}jas cikk.}, month = {Jan 2011}, pages = {206 - 220}, publisher = {NJSZT}, organization = {NJSZT}, type = {Conference paper}, address = {Szeged}, url = {http://www.inf.u-szeged.hu/kepaf2011/pdfs/S05_03.pdf}, author = {Csaba Domokos and Zoltan Kato}, editor = {Zoltan Kato and K{\'a}lm{\'a}n Pal{\'a}gyi} } @conference {1128, title = {Bin{\'a}ris tomogr{\'a}fiai rekonstrukci{\'o} objektum alap{\'u} evol{\'u}ci{\'o}s algoritmussal}, 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 = {117 - 127}, publisher = {NJSZT}, organization = {NJSZT}, type = {Conference paper}, address = {Szeged} } @conference {1221, title = {{\'E}l{\H o}sejt szegment{\'a}l{\'a}sa gr{\'a}fv{\'a}g{\'a}s seg{\'\i}ts{\'e}g{\'e}vel fluoreszcenci{\'a}s mikroszk{\'o}p k{\'e}peken}, 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 = {319 - 328}, publisher = {NJSZT}, organization = {NJSZT}, type = {Conference paper}, address = {Szeged}, url = {http://www.inf.u-szeged.hu/kepaf2011/pdfs/S08_02.pdf}, author = {Milan Lesko and Zoltan Kato and Antal Nagy and Imre Gombos and Zsolt T{\"o}r{\"o}k and L{\'a}szl{\'o} V{\'\i}gh and L{\'a}szl{\'o} V{\'\i}gh}, editor = {Zoltan Kato and K{\'a}lm{\'a}n Pal{\'a}gyi} } @conference {1218, title = {Fast linear registration of 3D objects segmented from medical images}, booktitle = {Biomedical Engineering and Informatics (BMEI)}, year = {2011}, note = {ScopusID: 84855764850doi: 10.1109/BMEI.2011.6098290}, month = {Oct 2011}, pages = {294 - 298}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Shanghai}, abstract = {In this paper a linear registration framework is used for medical image registration using segmented binary objects. The method is best suited for problems where the segmentation is available, but we also propose a general bone segmentation approach for CT images. We focus on the case when the objects to be registered differ considerably because of segmentation errors. We check the applicability of the method to bone segmentation of pelvic and thoracic CT images. Comparison is also made against a classical mutual information-based registration method. {\textcopyright} 2011 IEEE.

}, isbn = {978-1-4244-9351-7 }, doi = {10.1109/BMEI.2011.6098290}, author = {Attila Tanacs and Zoltan Kato}, editor = {Yongsheng Ding and Yonghong Peng and Riyi Shi and Kuangrong Hao and Lipo Wang} } @conference {876, title = {Iter{\'a}ci{\'o}nk{\'e}nti sim{\'\i}t{\'a}ssal kombin{\'a}lt v{\'e}kony{\'\i}t{\'a}s}, 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 = {174 - 189}, publisher = {NJSZT}, organization = {NJSZT}, type = {Conference paper}, address = {Szeged}, url = {http://www.inf.u-szeged.hu/kepaf2011/pdfs/S05_01.pdf}, author = {P{\'e}ter Kardos and G{\'a}bor N{\'e}meth and K{\'a}lm{\'a}n Pal{\'a}gyi}, editor = {Zoltan Kato and K{\'a}lm{\'a}n Pal{\'a}gyi} } @conference {1129, title = {Medi{\'a}nsz{\H u}r{\'e}s alkalmaz{\'a}sa algebrai rekonstrukci{\'o}s m{\'o}dszerekben}, 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 = {106 - 116}, publisher = {NJSZT}, organization = {NJSZT}, type = {Conference paper}, address = {Szeged}, author = {Norbert Hantos and P{\'e}ter Bal{\'a}zs}, editor = {Zoltan Kato and K{\'a}lm{\'a}n Pal{\'a}gyi} } @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} } @conference {1226, title = {A non-linear diffeomorphic framework for prostate multimodal registration}, booktitle = {International Conference on Digital Image Computing: Techniques and Applications (DICTA)}, year = {2011}, note = {ScopusID: 84856980939doi: 10.1109/DICTA.2011.14}, month = {Dec 2011}, pages = {31 - 36}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Noosa, QLD }, abstract = {This paper presents a novel method for non-rigid registration of prostate multimodal images based on a nonlinear framework. The parametric estimation of the non-linear diffeomorphism between the 2D fixed and moving images has its basis in solving a set of non-linear equations of thin-plate splines. The regularized bending energy of the thin-plate splines along with the localization error of established correspondences is jointly minimized with the fixed and transformed image difference, where, the transformed image is represented by the set of non-linear equations defined over the moving image. The traditional thin-plate splines with established correspondences may provide good registration of the anatomical targets inside the prostate but may fail to provide improved contour registration. On the contrary, the proposed framework maintains the accuracy of registration in terms of overlap due to the non-linear thinplate spline functions while also producing smooth deformations of the anatomical structures inside the prostate as a result of established corrspondences. The registration accuracies of the proposed method are evaluated in 20 pairs of prostate midgland ultrasound and magnetic resonance images in terms of Dice similarity coefficient with an average of 0.982 {\textpm} 0.004, average 95\% Hausdorff distance of 1.54 {\textpm} 0.46 mm and mean target registration and target localization errors of 1.90{\textpm}1.27 mm and 0.15 {\textpm} 0.12 mm respectively. {\textcopyright} 2011 IEEE.

}, isbn = {978-1-4577-2006-2 }, doi = {10.1109/DICTA.2011.14}, author = {Jhimli Mitra and Zoltan Kato and Robert Mart{\'\i} and Oliver Arnau and Xavier Llad{\'o} and Soumya Ghose and Joan C Vilanova and Fabrice Meriaudeau} } @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} } @book {1217, title = {Sz{\'a}m{\'\i}t{\'o}g{\'e}pes l{\'a}t{\'a}s}, year = {2011}, month = {2011}, publisher = {Typotex Kiad{\'o}}, organization = {Typotex Kiad{\'o}}, type = {Book}, address = {Budapest}, author = {Zoltan Kato and L{\'a}szl{\'o} Cz{\'u}ni} } @conference {877, title = {A topol{\'o}gia-meg{\H o}rz{\'e}s elegend{\H o} felt{\'e}telein alapul{\'o} 3D p{\'a}rhuzamos v{\'e}kony{\'\i}t{\'o} algoritmusok}, 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 = {190 - 205}, publisher = {NJSZT}, organization = {NJSZT}, type = {Conference paper}, address = {Szeged}, url = {http://www.inf.u-szeged.hu/kepaf2011/pdfs/S05_02.pdf}, author = {G{\'a}bor N{\'e}meth and P{\'e}ter Kardos and K{\'a}lm{\'a}n Pal{\'a}gyi}, editor = {Zoltan Kato and K{\'a}lm{\'a}n Pal{\'a}gyi} } @conference {1130, title = {Vet{\"u}leti ir{\'a}nyf{\"u}gg{\H o}s{\'e}g a bin{\'a}ris tomogr{\'a}fi{\'a}ban}, 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 = {92 - 105}, publisher = {NJSZT}, organization = {NJSZT}, type = {Conference paper}, address = {Szeged}, author = {L{\'a}szl{\'o} G{\'a}bor Varga and P{\'e}ter Bal{\'a}zs and Antal Nagy}, editor = {Zoltan Kato and K{\'a}lm{\'a}n Pal{\'a}gyi} } @inbook {1244, title = {Affine puzzle: Realigning deformed object fragments without correspondences}, booktitle = {European Conference on Computer Vision (ECCV)}, series = {Lecture Notes in Computer Science}, number = {6312}, year = {2010}, note = {UT: 000286164000056ScopusID: 78149337447doi: 10.1007/978-3-642-15552-9_56}, month = {Sep 2010}, pages = {777 - 790}, publisher = {Springer}, organization = {Springer}, type = {Conference paper}, address = {Crete, Greece}, abstract = {This paper is addressing the problem of realigning broken objects without correspondences. We consider linear transformations between the object fragments and present the method through 2D and 3D affine transformations. The basic idea is to construct and solve a polynomial system of equations which provides the unknown parameters of the alignment. We have quantitatively evaluated the proposed algorithm on a large synthetic dataset containing 2D and 3D images. The results show that the method performs well and robust against segmentation errors. We also present experiments on 2D real images as well as on volumetric medical images applied to surgical planning. {\textcopyright} 2010 Springer-Verlag.

}, isbn = {978-3-642-15551-2}, issn = {0302-9743}, doi = {10.1007/978-3-642-15552-9_56}, author = {Csaba Domokos and Zoltan Kato}, editor = {Kostas Daniilidis and Petros Maragos and Nikos Paragios} } @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 {1350, title = {Live cell segmentation in fluorescence microscopy via graph cut}, booktitle = {20th international conference on pattern recognition (ICPR 2010)}, year = {2010}, note = {ScopusID: 78149486419doi: 10.1109/ICPR.2010.367Besorol{\'a}s: Konferenciak{\"o}zlem{\'e}ny}, month = {Aug 2010}, pages = {1485 - 1488}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Istanbul, Turkey}, abstract = {We propose a novel Markovian segmentation model which takes into account edge information. By construction, the model uses only pairwise interactions and its energy is submodular. Thus the exact energy minima is obtained via a max-flow/min-cut algorithm. The method has been quantitatively evaluated on synthetic images as well as on fluorescence microscopic images of live cells. {\textcopyright} 2010 IEEE.

}, isbn = {978-1-4244-7542-1 }, issn = {1051-4651 }, doi = {10.1109/ICPR.2010.367 }, author = {Milan Lesko and Zoltan Kato and Antal Nagy and Imre Gombos and Zsolt T{\"o}r{\"o}k and L{\'a}szl{\'o} V{\'\i}gh and L{\'a}szl{\'o} V{\'\i}gh}, editor = {Aytul Ercil} } @article {1212, title = {Parametric estimation of affine deformations of planar shapes}, journal = {PATTERN RECOGNITION}, volume = {43}, year = {2010}, note = {UT: 000273094100003doi: 10.1016/j.patcog.2009.08.013}, month = {March 2010}, pages = {569 - 578}, type = {Journal article}, isbn = {0031-3203}, doi = {10.1016/j.patcog.2009.08.013}, author = {Csaba Domokos and Zoltan Kato} } @inbook {1247, title = {SITIS 2010: Track SIT editorial message: Signal and Image Technologies}, booktitle = {Proceedings of the 6th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2010}, year = {2010}, note = {ScopusID: 79952549721}, month = {2010}, pages = {XV}, publisher = {IEEE Computer Society Press}, organization = {IEEE Computer Society Press}, address = {Kuala Lumpur}, author = {Albert Dipanda and Zoltan Kato}, editor = {Albert Dipanda and Richard Chbeir and Kokou Yetongnon} } @inbook {1229, title = {Affine alignment of compound objects: A direct approach}, booktitle = {16th IEEE International Conference on Image Processing (ICIP), 2009}, year = {2009}, note = {UT: 000280464300043ScopusID: 77951939917doi: 10.1109/ICIP.2009.5414195}, month = {Nov 2009}, pages = {169 - 172}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Cairo, Egypt}, abstract = {A direct approach for parametric estimation of 2D affine deformations between compound shapes is proposed. It provides the result as a least-square solution of a linear system of equations. The basic idea is to fit Gaussian densities over the objects yielding covariant functions, which preserves the effect of the unknown transformation. Based on these functions, linear equations are constructed by integrating nonlinear functions over appropriate domains. The main advantages are: linear complexity, easy implementation, works without any time consuming optimization or established correspondences. Comparative tests show that it outperforms state-of-the-art methods both in terms of precision, robustness and complexity. {\textcopyright}2009 IEEE.

}, isbn = {978-1-4244-5653-6 }, issn = {1522-4880 }, doi = {10.1109/ICIP.2009.5414195 }, author = {Csaba Domokos and Zoltan Kato} } @article {1284, title = {Affine Registration of Planar Shapes}, year = {2009}, month = {2009///}, abstract = {This is the sample implementation and benchmark dataset of the binary image registration algorithm described in the following paper: Csaba Domokos and Zoltan Kato. Parametric Estimation of Affine Deformations of Planar Shapes. Pattern Recognition, 43(3):569--578, March 2010.

}, url = {http://www.inf.u-szeged.hu/~kato/software/affbinregdemo.html}, author = {Zsolt Katona and Csaba Domokos and Zoltan Kato} } @article {1211, title = {Detection of Object Motion Regions in Aerial Image Pairs with a Multilayer Markovian Model}, journal = {IEEE TRANSACTIONS ON IMAGE PROCESSING}, volume = {18}, year = {2009}, note = {UT: 000269715500013ScopusID: 70349442338doi: 10.1109/TIP.2009.2025808}, month = {2009}, pages = {2303 - 2315}, publisher = {IEEE}, type = {Journal article}, abstract = {We propose a new Bayesian method for detectingthe regions of object displacements in aerial image pairs. We use a robust but coarse 2-D image registration algorithm. Our main challenge is to eliminate the registration errors from the extracted change map. We introduce a three-layer Markov Random Field (L3MRF) model which integrates information from two different features, and ensures connected homogenous regions in the segmented images. Validation is given on real aerial photos.

}, isbn = {1057-7149}, doi = {10.1109/TIP.2009.2025808 }, author = {Csaba Benedek and Tamas Sziranyi and Zoltan Kato and Josiane Zerubia} } @article {1213, title = {A higher-order active contour model of a {\textquoteright}gas of circles{\textquoteright} and its application to tree crown extraction}, journal = {PATTERN RECOGNITION}, volume = {42}, year = {2009}, note = {UT: 000263431200011doi: 10.1016/j.patcog.2008.09.008}, month = {2009///}, pages = {699 - 709}, isbn = {0031-3203}, author = {Peter Horvath and Ian Jermyn and Zoltan Kato and Josiane Zerubia} } @conference {1274, title = {K{\"o}r alak{\'u} objektumok szegment{\'a}l{\'a}sa Markov mez{\H o} seg{\'\i}ts{\'e}g{\'e}vel}, 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 2009}, year = {2009}, note = {Received the Attila Kuba Prize}, month = {Jan 2009}, pages = {1 - 9}, publisher = {Akaprint}, organization = {Akaprint}, type = {Conference paper}, address = {Budapest}, url = {http://vision.sztaki.hu/~kepaf/kepaf2009_CD/files/116-4-MRFCircle08.pdf} } @inbook {1231, title = {A Markov random field model for extracting near-circular shapes}, booktitle = {16th IEEE International Conference on Image Processing (ICIP)}, year = {2009}, note = {UT: 000280464300268ScopusID: 77951945383doi: 10.1109/ICIP.2009.5413472}, month = {Nov 2009}, pages = {1073 - 1076}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Cairo, Egypt}, abstract = {We propose a binary Markov Random Field (MRF) model that assigns high probability to regions in the image domain consisting of an unknown number of circles of a given radius. We construct the model by discretizing the {\textquoteright}gas of circles{\textquoteright} phase field model in a principled way, thereby creating an {\textquoteright}equivalent{\textquoteright}MRF. The behaviour of the resultingMRF model is analyzed, and the performance of the new model is demonstrated on various synthetic images as well as on the problem of tree crown detection in aerial images. {\textcopyright}2009 IEEE.

}, isbn = {978-1-4244-5653-6 }, doi = {10.1109/ICIP.2009.5413472 }, author = {Tam{\'a}s Blaskovics and Zoltan Kato and Ian Jermyn} } @conference {1230, title = {Nonlinear registration of binary shapes}, booktitle = {16th IEEE International Conference on Image Processing (ICIP)}, year = {2009}, note = {UT: 000280464300275ScopusID: 77951946286doi: 10.1109/ICIP.2009.5413468}, month = {Nov 2009}, pages = {1101 - 1104}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Cairo, Egypt}, abstract = {A novel approach is proposed to estimate the parameters of a diffeomorphism that aligns two binary images. Classical approaches usually define a cost function based on a similarity metric and then find the solution via optimization. Herein, we trace back the problem to the solution of a system of non-linear equations which directly provides the parameters of the aligning transformation. The proposed method works without any time consuming optimization step or established correspondences. The advantage of our algorithm is that it is easy to implement, less sensitive to the strength of the deformation, and robust against segmentation errors. The efficiency of the proposed approach has been demonstrated on a large synthetic dataset as well as in the context of an industrial application. {\textcopyright}2009 IEEE.

}, isbn = {978-1-4244-5653-6 }, doi = {10.1109/ICIP.2009.5413468 }, author = {Jozsef Nemeth and Csaba Domokos 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} } @inbook {1228, title = {Recovering planar homographies between 2D shapes}, booktitle = {12th International Conference on Computer Vision, ICCV 2009}, year = {2009}, note = {UT: 000294955300280ScopusID: 77953177385doi: 10.1109/ICCV.2009.5459474}, month = {2009///}, pages = {2170 - 2176}, publisher = {IEEE}, organization = {IEEE}, abstract = {Images taken from different views of a planar object are related by planar homography. Recovering the parameters of such transformations is a fundamental problem in computer vision with various applications. This paper proposes a novel method to estimate the parameters of a homography that aligns two binary images. It is obtained by solving a system of nonlinear equations generated by integrating linearly independent functions over the domains determined by the shapes. The advantage of the proposed solution is that it is easy to implement, less sensitive to the strength of the deformation, works without established correspondences and robust against segmentation errors. The method has been tested on synthetic as well as on real images and its efficiency has been demonstrated in the context of two different applications: alignment of hip prosthesis X-ray images and matching of traffic signs. {\textcopyright}2009 IEEE.

} } @conference {1291, title = {S{\'\i}kbeli alakzatok regisztr{\'a}ci{\'o}ja kovari{\'a}ns f{\"u}ggv{\'e}nyek felhaszn{\'a}l{\'a}s{\'a}val}, 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 2009}, year = {2009}, month = {Jan 2009}, pages = {1 - 8}, publisher = {Akaprint}, organization = {Akaprint}, type = {Conference papers}, address = {Budapest} } @conference {1292, title = {S{\'\i}khomogr{\'a}fia param{\'e}tereinek becsl{\'e}se bin{\'a}ris k{\'e}peken}, 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 2009}, year = {2009}, month = {Jan 2009}, pages = {1 - 8}, publisher = {Akaprint}, organization = {Akaprint}, type = {Conference paper}, address = {Budapest} } @booklet {1283, title = {Supervised Color Image Segmentation in a Markovian Framework}, year = {2009}, month = {2009///}, abstract = {This is the sample implementation of a Markov random field based color image segmentation algorithm described in the following paper: Zoltan Kato, Ting Chuen Pong, and John Chung Mong Lee. Color Image Segmentation and Parameter Estimation in a Markovian Framework. Pattern Recognition Letters, 22(3-4):309--321, March 2001. Note that the current demo program implements only a supervised version of the segmentation method described in the above paper (i.e. parameter values are learned interactively from representative regions selected by the user). Otherwise, the program implements exactly the color MRF model proposed in the paper. Images are automatically converted from RGB to the perceptually uniform CIE-L*u*v* color space before segmentation.

}, url = {http://www.inf.u-szeged.hu/~kato/software/colormrfdemo.html}, author = {Mih{\'a}ly Gara and Zoltan Kato} } @inbook {1245, title = {Binary image registration using covariant gaussian densities}, booktitle = {Image Analysis and Recognition}, series = {Lecture Notes in Computer Science}, number = {5112}, year = {2008}, note = {UT: 000257302500045ScopusID: 47749098390doi: 10.1007/978-3-540-69812-8_45}, month = {June 2008}, pages = {455 - 464}, publisher = {Springer}, organization = {Springer}, type = {Conference paper}, address = {P{\'o}voa de Varzim, Portugal}, abstract = {We consider the estimation of 2D affine transformations aligning a known binary shape and its distorted observation. The classical way to solve this registration problem is to find correspondences between the two images and then compute the transformation parameters from these landmarks. In this paper, we propose a novel approach where the exact transformation is obtained as a least-squares solution of a linear system. The basic idea is to fit a Gaussian density to the shapes which preserves the effect of the unknown transformation. It can also be regarded as a consistent coloring of the shapes yielding two rich functions defined over the two shapes to be matched. The advantage of the proposed solution is that it is fast, easy to implement, works without established correspondences and provides a unique and exact solution regardless of the magnitude of transformation. {\textcopyright} 2008 Springer-Verlag Berlin Heidelberg.

}, isbn = {978-3-540-69811-1}, doi = {10.1007/978-3-540-69812-8_45}, author = {Csaba Domokos and Zoltan Kato}, editor = {Aur{\'e}lio Campilho} } @inbook {1172, title = {A k{\'e}pfeldolgoz{\'a}s kutat{\'a}sa a Szegedi Tudom{\'a}nyegyetemen}, booktitle = {Informatika a fels{\H o}oktat{\'a}sban 2008}, year = {2008}, note = {Art. No.: E62}, month = {2008///}, publisher = {Debreceni Egyetem Informatikai Kar}, organization = {Debreceni Egyetem Informatikai Kar}, address = {Debrecen}, abstract = {A digit{\'a}lis k{\'e}pfeldolgoz{\'a}s kutat{\'a}s{\'a}nak a Szegedi Tudom{\'a}nyegyetemTerm{\'e}szettudom{\'a}nyi {\'e}s Informatikai Kar{\'a}n, az Informatikai Tansz{\'e}kcsoport K{\'e}pfeldolgoz{\'a}s {\'e}s Sz{\'a}m{\'\i}t{\'o}g{\'e}pes Grafika Tansz{\'e}k{\'e}n k{\"o}zel n{\'e}gy {\'e}vtizedes hagyom{\'a}nya van. A Tansz{\'e}k valamennyi munkat{\'a}rsa nemzetk{\"o}zileg elismert kutat{\'o}munk{\'a}t folytat, melyet m{\'a}r t{\"o}bb sz{\'a}z rangos publik{\'a}ci{\'o} f{\'e}mjelez. Sz{\'a}mos, a k{\'e}pfeldolgoz{\'a}s kutat{\'a}s{\'a}ban vezet{\H o} egyetemmel {\'e}s kutat{\'o}int{\'e}zettel {\'e}p{\'\i}tett{\"u}nk ki szoros kapcsolatot {\'e}s folytattunk eredm{\'e}nyes kutat{\'o}munk{\'a}t, akt{\'\i}v r{\'e}sztvev{\H o}i vagyunk a hazai {\'e}s a nemzetk{\"o}zi tudom{\'a}nyos k{\"o}z{\'e}letnek. A legfontosabb, jelenleg is foly{\'o} kutat{\'a}saink: orvosi k{\'e}pek feldolgoz{\'a}sa, diszkr{\'e}t tomogr{\'a}fia, k{\'e}pszegment{\'a}l{\'a}s, t{\'e}rinformatika, t{\'a}v{\'e}rz{\'e}kel{\'e}s, k{\'e}pregisztr{\'a}ci{\'o}, v{\'a}zkijel{\"o}l{\'e}s, m{\H u}t{\'e}ti tervez{\'e}s. }, url = {http://www.agr.unideb.hu/if2008/kiadvany/papers/E62.pdf} } @inbook {1232, title = {Parametric estimation of affine deformations of binary images}, booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year = {2008}, note = {UT: 000257456700223ScopusID: 51449098982doi: 10.1109/ICASSP.2008.4517753}, month = {March 2008}, pages = {889 - 892}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Las Vegas, NV, USA}, abstract = {We consider the problem of planar object registration on binary images where the aligning transformation is restricted to the group of affine transformations. Previous approaches usually require established correspondences or the solution of nonlinear optimization problems. Herein we show that it is possible to formulate the problem as the solution of a system of up to third order polynomial equations. These equations are constructed in a simple way using some basic geometric information of binary images. It does not need established correspondences nor the solution of complex optimization problems. The resulting algorithm is fast and provides a direct solution regardless of the magnitude of transformation. {\textcopyright}2008 IEEE.

}, isbn = {978-1-4244-1483-3 }, issn = {1520-6149 }, doi = {10.1109/ICASSP.2008.4517753 }, author = {Csaba Domokos and Zoltan Kato and Joseph M Francos} } @article {1214, title = {Segmentation of color images via reversible jump MCMC sampling}, journal = {IMAGE AND VISION COMPUTING}, volume = {26}, year = {2008}, note = {UT: 000252196500005doi: 10.1016/j.imavis.2006.12.004}, month = {March 2008}, pages = {361 - 371}, publisher = {Elsevier}, type = {Journal article}, isbn = {0262-8856}, doi = {10.1016/j.imavis.2006.12.004}, author = {Zoltan Kato} } @conference {1275, title = {K{\"o}r alak{\'u} objektumok szegment{\'a}l{\'a}sa magasabb rend{\H u} akt{\'\i}v kont{\'u}r modellek seg{\'\i}ts{\'e}g{\'e}vel}, 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 2007}, year = {2007}, month = {Jan 2007}, pages = {133 - 140}, publisher = {K{\'e}pfeldolgoz{\'o}k {\'e}s Alakfelismer{\H o}k T{\'a}rsas{\'a}ga}, organization = {K{\'e}pfeldolgoz{\'o}k {\'e}s Alakfelismer{\H o}k T{\'a}rsas{\'a}ga}, type = {Conference paper}, address = {Debrecen} } @mastersthesis {1264, title = {Markovian Image Models and their Application in Image Segmentation}, year = {2007}, month = {2007}, type = {PhD Thesis}, author = {Zoltan Kato} } @conference {1293, title = {Parametric Estimation of Two-Dimensional Affine Transformations of Binary Images}, 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 2007}, year = {2007}, month = {Jan 2007}, pages = {257 - 265}, publisher = {K{\'e}pfeldolgoz{\'o}k {\'e}s Alakfelismer{\H o}k T{\'a}rsas{\'a}ga}, organization = {K{\'e}pfeldolgoz{\'o}k {\'e}s Alakfelismer{\H o}k T{\'a}rsas{\'a}ga}, type = {Conference paper}, address = {Debrecen} } @booklet {1266, title = {A Three-layer MRF model for Object Motion Detection in Airborne Images}, year = {2007}, month = {2007///}, author = {Csaba Benedek and Tamas Sziranyi and Zoltan Kato and Josiane Zerubia} } @inbook {1233, title = {A higher-order active contour model for tree detection}, booktitle = {Proceedings of the18th International Conference on Pattern Recognition, ICPR 2006}, year = {2006}, note = {ScopusID: 34047219865doi: 10.1109/ICPR.2006.79}, month = {2006///}, pages = {130 - 133}, publisher = {IEEE}, organization = {IEEE}, abstract = {We present a model of a {\textquoteright}gas of circles{\textquoteright}, the ensemble of regions in the image domain consisting of an unknown number of circles with approximately fixed radius and short range repulsive interactions, and apply it to the extraction of tree crowns from aerial images. The method uses the recently introduced {\textquoteright}higher order active contours{\textquoteright} (HOACs), which incorporate long-range interactions between contour points, and thereby include prior geometric information without using a template shape. This makes them ideal when looking for multiple instances of an entity in an image. We study an existing HOAC model for networks, and show via a stability calculation that circles stable to perturbations are possible for constrained parameter sets. Combining this prior energy with a data term, we show results on aerial imagery that demonstrate the effectiveness of the method and the need for prior geometric knowledge. The model has many other potential applications. {\textcopyright} 2006 IEEE.

} } @conference {Horvath-etal2006, title = {A Higher-Order Active Contour Model for Tree Detection}, booktitle = {Proceedings of the International Conference on Pattern Recognition (ICPR)}, volume = {2}, year = {2006}, month = {2006}, pages = {130{\textendash}133}, publisher = {IAPR}, organization = {IAPR}, type = {Conference paper}, address = {Hong Kong, China}, abstract = {We present a model of a {\textquoteright}gas of circles{\textquoteright}, the ensemble of regions in the image domain consisting of an unknown number of circles with approximately fixed radius and short range repulsive interactions, and apply it to the extraction of tree crowns from aerial images. The method uses the recently introduced {\textquoteright}higher order active contours{\textquoteright} (HOACs), which incorporate long-range interactions between contour points, and thereby include prior geometric information without using a template shape. This makes them ideal when looking for multiple instances of an entity in an image. We study an existing HOAC model for networks, and show via a stability calculation that circles stable to perturbations are possible for constrained parameter sets. Combining this prior energy with a data term, we show results on aerial imagery that demonstrate the effectiveness of the method and the need for prior geometric knowledge. The model has many other potential applications. ` `

Az SZTE Informatikai Tansz{\'e}kcsoportja {\'a}ltal gondozott szakoktanterveiben 1993 {\'o}ta szerepel a k{\'e}pfeldolgoz{\'a}s {\'e}s alkalmaz{\'a}sainak oktat{\'a}sa. A kreditrendszer bevezet{\'e}s{\'e}vel a K{\'e}pfeldolgoz{\'a}s I. t{\'a}rgy k{\"o}telez{\H o} az {\"o}t{\'e}ves k{\'e}pz{\'e}sben r{\'e}szt vev{\H o} informatikus hallgat{\'o}knak. Ezen fel{\"u}l a v{\'a}laszthat{\'o} szakir{\'a}nyok k{\"o}z{\"o}tt szint{\'e}n szerepel a K{\'e}pfeldolgoz{\'a}s szakir{\'a}ny. A szakir{\'a}nyon bel{\"u}l k{\"u}l{\"o}nb{\"o}z{\H o} k{\'e}ppfeldolgoz{\'a}si ter{\"u}leteket t{\'a}rgyal{\'o} kurzusok {\'e}p{\"u}lnek egym{\'a}sra. Az elm{\'e}leti megalapoz{\'a}s mellett a k{\'e}pfeldolgoz{\'a}s alkalmaz{\'a}saira is nagy hangs{\'u}lyt fektet{\"u}nk. A kutat{\'a}sok illetve az orvosi alkalmaz{\'a}sok fejleszt{\'e}se sor{\'a}n szerzett eredm{\'e}nyeket a k{\"o}telez{\H o} jelleg{\H u} t{\'a}rgyak mellett speci{\'a}lkoll{\'e}giumok keret{\'e}ben {\'e}p{\'\i}tj{\"u}l be az otkat{\'a}si anyagba. Sz{\'a}mos hallgat{\'o}nk v{\'a}laszt a k{\'e}pfeldolgz{\'a}s ter{\"u}let{\'e}r{\H o}l t{\'e}m{\'a}t a diplomamunk{\'a}j{\'a}hoz, dolgozataikkal rendszeresen {\'e}s sikerrel szerepelnek az OTDK-n. Hallgat{\'o}ink {\'e}vente t{\"o}bb h{\'o}napot t{\"o}lthetnek k{\"u}lf{\"o}ldi partneregyetemeinken, ahol a kutat{\'o}- {\'e}s fejleszt{\H o}munka mellett n{\'a}lunk is elfogadott kurzusokat teljes{\'\i}thetnek. A k{\'e}pfeldolgoz{\'a}s t{\'e}mak{\"o}r{\"o}n bel{\"u}l "ipari" projekt munk{\'a}kban is egyre t{\"o}bb hallgat{\'o} vesz r{\'e}szt. A doktori programon bel{\"u}l is meghirdet{\"u}nk k{\'e}pfeldolgoz{\'a}shoz kapcsol{\'o}d{\'o} kutat{\'a}si ir{\'a}nyokat. Az {\'e}vente megrendez{\'e}sre ker{\"u}l{\H o}, 11-{\'e}ves m{\'u}ltra visszatekint{\H o} K{\'e}pfeldolgoz{\'o} Ny{\'a}ri Iskol{\'a}nak (SSIP) eddig hatszor adott otthont Szeged. A rendszv{\'e}nysorozat kiemelked{\H o} fontoss{\'a}g{\'u} nemzetk{\"o}zi f{\'o}rum hallgat{\'o}ink {\'e}s oktat{\'o}ink sz{\'a}m{\'a}ra is.

} } @inbook {1257, title = {Non-Photorealistic Rendering and Content-Based Image Retrieval}, booktitle = {Proceedings of 11th Pacific Conference on Computer Graphics and Applications (PG)}, year = {2003}, note = {doi: 10.1109/PCCGA.2003.1238257}, month = {2003///}, pages = {153 - 162}, publisher = {IEEE Computer Soc. Pr.}, organization = {IEEE Computer Soc. Pr.}, address = {New York} } @inbook {1234, title = {Unsupervised segmentation of color textured images using a multi-layer MRF model}, booktitle = {ICIP 2003: IEEE International Conference on Image Processing}, year = {2003}, note = {ScopusID: 0344666539doi: 10.1109/ICIP.2003.1247124}, month = {2003///}, pages = {961 - 964}, publisher = {IEEE}, organization = {IEEE}, abstract = {Herein, we propose a novel multi-layer Markov random field (MRF) image segmentation model which aims at combining color and texture features: Each feature is associated to a so called feature layer, where an MRF model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model. This layer interacts with each feature layer and provides the segmentation based on the combination of different features. The model is quite generic and isn{\textquoteright}t restricted to a particular texture feature. Herein we will test the algorithm using Gabor and MRSAR texture features. Furthermore, the algorithm automatically estimates the number of classes at each layer (there can be different classes at different layers) and the associated model parameters.

} } @conference {1209, title = {Content-based image retrieval using stochastic paintbrush transformation}, booktitle = {IEEE - International Conference on Image Processing: ICIP}, year = {2002}, note = {UT: 000185208200237}, month = {Sep 2002}, pages = {944 - 947}, publisher = {IEEE Computer Society Press}, organization = {IEEE Computer Society Press}, address = {Aix-en-Provence} } @article {1278, title = {Markov random fields in image processing application to remote sensing and astrophysics}, journal = {JOURNAL DE PHYSIQUE IV}, volume = {12}, year = {2002}, note = {UT: 000175261200006doi: 10.1051/jp42002005}, month = {2002///}, pages = {117 - 136}, isbn = {1155-4339} } @inbook {1246, title = {Multicue MRF image segmentation: Combining texture and color features}, booktitle = {Proceedings 16th International Conference on Pattern Recognition (ICPR 2002)}, year = {2002}, note = {ScopusID: 33751583776}, month = {2002///}, pages = {660 - 663}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, abstract = {Herein, we propose a new Markov random field (MRF) image segmentation model which aims at combining color and texture features. The model has a multi-layer structure: Each feature has its own layer, called feature layer, where an MRF model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model. This layer interacts with each feature layer and provides the segmentation based on the combination of different features. The uniqueness of our algorithm is that it provides both color only and texture only segmentations as well as a segmentation based on combined color and texture features. The number of classes on feature layers is given by the user but it is estimated on the combined layer. {\textcopyright} 2002 IEEE.

} } @article {1235, title = {Color image segmentation and parameter estimation in a markovian framework}, journal = {PATTERN RECOGNITION LETTERS}, volume = {22}, year = {2001}, note = {UT: 000167983900005ScopusID: 0035272740doi: 10.1016/S0167-8655(00)00106-9}, month = {2001///}, pages = {309 - 321}, abstract = {An unsupervised color image segmentation algorithm is presented, using a Markov random field (MRF) pixel classification model. We propose a new method to estimate initial mean vectors effectively even if the histogram does not have clearly distinguishable peaks. The only parameter supplied by the user is the number of classes. {\textcopyright} 2001 Elsevier Science B.V. All rights reserved.

}, isbn = {0167-8655} } @inbook {1258, title = {A Markov Random Field Image Segmentation Model Using Combined Color and Texture Features}, booktitle = {Proceedings of International Conference on Computer Analysis of Images and Patterns (CAIP)}, year = {2001}, note = {doi: 10.1007/3-540-44692-3_66}, month = {2001///}, pages = {547 - 554}, publisher = {Springer Verlag}, organization = {Springer Verlag}, address = {Berlin; Heidelberg} } @article {1207, title = {Image segmentation using Markov random field model in fully parallel cellular network architectures}, journal = {REAL-TIME IMAGING}, volume = {6}, year = {2000}, note = {UT: 000088331700003ScopusID: 0034204755}, month = {2000///}, pages = {195 - 211}, isbn = {1077-2014}, url = {http://www.sztaki.hu/~sziranyi/Papers/Sziranyi_MRF.pdf} } @booklet {1268, title = {Bayesian Color Image Segmentation Using Reversible Jump Markov Chain Monte Carlo}, year = {1999}, note = {http://dl.acm.org/citation.cfm?id=869110http://dl.acm.org/citation.cfm?id=869110}, month = {1999///} } @article {1873, title = {Bayesian Color Image Segmentation Using Reversible Jump Markov Chain Monte Carlo}, number = {01/99-R055}, year = {1999}, month = {January 1999}, institution = {ERCIM/CWI}, type = {Research Report}, address = {Amsterdam, The Netherlands}, author = {Zoltan Kato} } @article {1236, title = {Unsupervised parallel image classification using Markovian models}, journal = {PATTERN RECOGNITION}, volume = {32}, year = {1999}, note = {UT: 000079145300005ScopusID: 0033116536doi: 10.1016/S0031-3203(98)00104-6}, month = {1999///}, pages = {591 - 604}, abstract = {This paper deals with the problem of unsupervised classification of images modeled by Markov random fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing (SA), iterated conditional modes (ICM), etc). However, when the parameters are unknown, the problem becomes more difficult. One has to estimate the hidden label field parameters only from the observed image. Herein, we are interested in parameter estimation methods related to monogrid and hierarchical MRF models. The basic idea is similar to the expectation-maximization (EM) algorithm: we recursively look at the maximum a posteriori (MAP) estimate of the label field given the estimated parameters, then we look at the maximum likelihood (ML) estimate of the parameters given a tentative labeling obtained at the previous step. The only parameter supposed to be known is the number of classes, all the other parameters are estimated. The proposed algorithms have been implemented on a Connection Machine CM200. Comparative experiments have been performed on both noisy synthetic data and real images. {\textcopyright} 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

}, isbn = {0031-3203} } @inbook {1259, title = {Motion Compensated Color Video Classification Using Markov Random Fields}, booktitle = {Proceedings of Asian Conference on Computer Vision (ACCV)}, year = {1998}, note = {doi: 10.1007/3-540-63930-6_189}, month = {1998///}, pages = {738 - 745}, publisher = {Springer Verlag}, organization = {Springer Verlag}, address = {Berlin; Heidelberg} } @conference {1260, title = {Color Image Classification and Parameter Estimation in a Markovian FrameworkProceedings of Workshop on 3D Computer Vision}, year = {1997}, month = {1997.05}, pages = {75 - 79}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.128.1560} } @booklet {1205, title = {Image segmentation using Markov random field model in fully parallel cellular network architectures.}, year = {1997}, month = {1997///}, pages = { - 17} } @booklet {1280, title = {Markov Random Field Image Segmentation using Cellular Neural Network}, year = {1997}, month = {1997///} } @booklet {1269, title = {Motion Compensated Color Image Classification and Parameter Estimation in a Markovian Framework}, year = {1997}, month = {1997///}, url = {http://biblioteca.universia.net/html_bura/ficha/params/title/motion-compensated-color-image-classification-and-parameter-estimation-in-markovian/id/5664082.html} } @conference {1206, title = {MRF based image segmentation with fully parallel cellular nonlinear networks}, 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 1997}, year = {1997}, month = {Oct 1997}, pages = {43 - 50}, publisher = {Pannon Agr{\'a}rtudom{\'a}nyi Egyetem Georgikon Mez{\H o}gazdas{\'a}gtudom{\'a}nyi Kar}, organization = {Pannon Agr{\'a}rtudom{\'a}nyi Egyetem Georgikon Mez{\H o}gazdas{\'a}gtudom{\'a}nyi Kar}, address = {Keszthely} } @article {1237, title = {Bayesian image classification using Markov random fields}, journal = {IMAGE AND VISION COMPUTING}, volume = {14}, year = {1996}, note = {UT: A1996UT58100004ScopusID: 0030148684doi: 10.1016/0262-8856(95)01072-6}, month = {1996///}, pages = {285 - 295}, abstract = {In this paper, we present three optimisation techniques, Deterministic Pseudo-Annealing (DPA), Game Strategy Approach (GSA), and Modified Metropolis Dynamics (MMD), in order to carry out image classification using a Markov random field model. For the first approach (DPA), the a posteriori probability of a tentative labelling is generalised to a continuous labelling. The merit function thus defined has the same maxima under constraints yielding probability vectors. Changing these constraints convexifies the merit function. The algorithm solves this unambiguous maximisation problem, and then tracks down the solution while the original constraints are restored yielding a good, even if suboptimal, solution to the original labelling assignment problem. In the second method (GSA), the maximisation problem of the a posteriori probability of the labelling is solved by an optimisation algorithm based on game theory. A non-cooperative n-person game with pure strategies is designed such that the set of Nash equilibrium points of the game is identical to the set of local maxima of the a posteriori probability of the labelling. The algorithm converges to a Nash equilibrium. The third method (MMD) is a modified version of the Metropolis algorithm: at each iteration the new state is chosen randomly, but the decision to accept it is purely deterministic. This is also a suboptimal technique but it is much faster than stochastic relaxation. These three methods have been implemented on a Connection Machine CM2. Experimental results are compared to those obtained by the Metropolis algorithm, the Gibbs sampler and ICM (Iterated Conditional Mode).

}, isbn = {0262-8856} } @inbook {1208, title = {Cellular Neural Network in Markov Random Field Image Segmentation}, booktitle = {1996 FOURTH IEEE INTERNATIONAL WORKSHOP ON CELLULAR NEURAL NETWORKS AND THEIR APPLICATIONS, PROCEEDINGS (CNNA-96)}, year = {1996}, note = {UT: A1996BH11L00025ScopusID: 0030409916Besorol{\'a}s: Konferenciak{\"o}zlem{\'e}ny}, month = {1996///}, pages = {139 - 144}, publisher = {Wiley - IEEE Press}, organization = {Wiley - IEEE Press}, address = {New York} } @article {1238, title = {A Hierarchical Markov Random Field Model and Multitemperature Annealing for Parallel Image Classification}, journal = {GRAPHICAL MODELS AND IMAGE PROCESSING}, volume = {58}, year = {1996}, note = {UT: A1996TZ03400002ScopusID: 0029732459doi: 10.1006/gmip.1996.0002}, month = {1996///}, pages = {18 - 37}, abstract = {In this paper, we are interested in massively parallel multiscale relaxation algorithms applied to image classification. It is well known that multigrid methods can improve significantly the convergence rate and the quality of the final results of iterative relaxation techniques. First, we present a classical multiscale model which consists of a label pyramid and a whole observation field. The potential functions of coarser grids are derived by simple computations. The optimization problem is first solved at the higher scale by a parallel relaxation algorithm; then the next lower scale is initialized by a projection of the result. Second, we propose a hierarchical Markov random field model based on this classical model. We introduce new interactions between neighbor levels in the pyramid. It can also be seen as a way to incorporate cliques with far apart sites for a reasonable price. This model results in a relaxation algorithm with a new annealing scheme: the multitemperature annealing (MTA) scheme, which consists of associating higher temperatures to higher levels, in order to be less sensitive to local minima at coarser grids. The convergence to the global optimum is proved by a generalization of the annealing theorem of S. Geman and D. Geman (IEEE Trans. Pattern Anal. Mach. Intell. 6, 1984, 721-741). {\textcopyright} 1996 Academic Press, Inc.

}, isbn = {1077-3169} } @article {1239, title = {DPA: a deterministic approach to the MAP problem}, journal = {IEEE TRANSACTIONS ON IMAGE PROCESSING}, volume = {4}, year = {1995}, note = {UT: A1995RT35400011ScopusID: 0029375669doi: 10.1109/83.413175}, month = {1995///}, pages = {1312 - 1314}, abstract = {Deterministic pseudo-annealing (DPA) is a new deterministic optimization method for finding the maximum a posteriori (MAP) labeling in a Markov random field, in which the probability of a tentative labeling is extended to a merit function on continuous labelings. This function is made convex by changing its definition domain. This unambiguous maximization problem is solved, and the solution is followed down to the original domain, yielding a good, if suboptimal, solution to the original labeling assignment problem. The performance of DPA is analyzed on randomly weighted graphs.}, isbn = {1057-7149} } @inbook {1240, title = {Unsupervised adaptive image segmentation}, booktitle = {ICASSP-95}, year = {1995}, note = {ScopusID: 0028996751doi: 10.1109/ICASSP.1995.479976}, month = {1995///}, pages = {2399 - 2402}, publisher = {IEEE}, organization = {IEEE}, address = {Piscataway}, abstract = {This paper deals with the problem of unsupervised Bayesian segmentation of images modeled by Markov Random Fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (Simulated Annealing, ICM, etc...). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the available image only. Our approach consists of a recent iterative method of estimation, called Iterative Conditional Estimation (ICE), applied to a monogrid Markovian image segmentation model. The method has been tested on synthetic and real satellite images.} } @inbook {1241, title = {Unsupervised parallel image classification using a hierarchical Markovian model}, booktitle = {Proceedings of the 5th International Conference on Computer Vision}, year = {1995}, note = {ScopusID: 0029214757doi: 10.1109/ICCV.1995.466790}, month = {1995///}, pages = {169 - 174}, publisher = {IEEE}, organization = {IEEE}, address = {Piscataway}, abstract = {This paper deals with the problem of unsupervised classification of images modeled by Markov Random Fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing, ICM, etc...). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the only observable image. Our approach consists of extending a recent iterative method of estimation, called Iterative Conditional Estimation (ICE) to a hierarchical markovian model. The idea resembles the Estimation-Maximization (EM) algorithm as we recursively look at the Maximum a Posteriori (MAP) estimate of the label field given the estimated parameters then we look at the Maximum Likelihood (ML) estimate of the parameters given a tentative labeling obtained at the previous step. We propose unsupervised image classification algorithms using a hierarchical model. The only parameter supposed to be known is the number of regions, all the other parameters are estimated. The presented algorithms have been implemented on a Connection Machine CM200. Comparative tests have been done on noisy synthetic and real images (remote sensing).} } @mastersthesis {1265, title = {Multi-scale Markovian Modelisation in Computer Vision with Applications to SPOT Image Segmentation : Mod{\'e}lisations markoviennes multir{\'e}solutions en vision par ordinateur. Application {\'r} la segmentation d{\textquoteright}images SPOT}, year = {1994}, month = {1994} } @inbook {1261, title = {Multi-Temperature Annealing: A New Approach for the Energy-Minimization of Hierarchical Markov Random Field Models}, booktitle = {Proceedings of the 12th IAPR International Conference on Pattern Recognition}, year = {1994}, note = {doi: 10.1109/ICPR.1994.576342}, month = {1994///}, pages = {520 - 522}, publisher = {IEEE}, organization = {IEEE}, address = {Los Alamitos} } @booklet {1281, title = {Segmentation hi{\'e}rarchique d{\textquoteright}images sur CM200 (Hierarchical Image Segmentation on the CM200)}, year = {1994}, month = {1994///} } @booklet {1279, title = {Segmentation multir{\'e}solution d{\textquoteright}images sur SUN version 1 du 26.05.1994 (Multiresolution Image Segmentation on SUN version 1 of 26.05.1994)}, year = {1994}, month = {1994///}, url = {http://www.app.asso.fr/en/} } @inbook {1250, title = {Bayesian Image Classification Using Markov Random Fields}, booktitle = {Maximum Entropy and Bayesian Methods}, year = {1993}, month = {1993///}, pages = {375 - 382}, publisher = {Kluwer Academic Publishers}, organization = {Kluwer Academic Publishers}, address = {Dordrecht; Boston; London} } @booklet {1271, title = {Extraction d{\textquoteright}information dans les images SPOT}, year = {1993}, month = {1993///} } @booklet {1272, title = {A Hierarchical Markov Random Field Model and Multi-Temperature Annealing for Parallel Image Classification}, year = {1993}, month = {1993///}, url = {http://hal.inria.fr/inria-00074736/} } @conference {1262, title = {A Hierarchical Markov Random Field Model for Image Classification}, booktitle = {International Workshop on Image and Multidimensional Digital Signal Processing (IMDSP)}, year = {1993}, note = {Art. No.: imdsp.ps}, month = {Sep 1993}, publisher = {IEEE Computer Soc. Pr.}, organization = {IEEE Computer Soc. Pr.} } @inbook {1242, title = {Multiscale Markov random field models for parallel image classification}, booktitle = {Fourth International Conference on Computer Vision, ICCV 1993, Berlin, Germany, 11-14 May, 1993, Proceedings}, year = {1993}, note = {ScopusID: 0027224261}, month = {1993///}, pages = {253 - 257}, publisher = {IEEE}, organization = {IEEE}, address = {Los Alamitos}, abstract = {In this paper, we are interested in multiscale Markov Random Field (MRF) models. It is well known that multigrid methods can improve significantly the convergence rate and the quality of the final results of iterative relaxation techniques. Herein, we propose a new hierarchical model, which consists of a label pyramid and a whole observation field. The parameters of the coarse grid can be derived by simple computation from the finest grid. In the label pyramid, we have introduced a new local interaction between two neighbor grids. This model gives a relaxation algorithm which can be run in parallel on the entire pyramid. On the other hand, the new model allows to propagate local interactions more efficiently giving estimates closer to the global optimum for deterministic as well as for stochastic relaxation schemes. It can also be seen as a way to incorporate cliques with far apart sites for a reasonable price.} } @inbook {1243, title = {Parallel image classification using multiscale Markov random fields}, booktitle = {ICASSP-93}, year = {1993}, note = {ScopusID: 0027266514doi: 10.1109/ICASSP.1993.319766}, month = {1993///}, pages = {137 - 140}, publisher = {IEEE}, organization = {IEEE}, address = {New York}, abstract = {In this paper, we are interested in massively parallel multiscale relaxation algorithms applied to image classification. First, we present a classical multiscale model applied to supervised image classification. The model consists of a label pyramid and a whole observation field. The potential functions of the coarse grid are derived by simple computations. Then, we propose another scheme introducing a local interaction between two neighbor grids in the label pyramid. This is a way to incorporate cliques with far apart sites for a reasonable price. Finally we present the results on noisy synthetic data and on a SPOT image obtained by different relaxation methods using these models.} } @booklet {1273, title = {Image Classification Using Markov Random Fields with Two New Relaxation Methods}, year = {1992}, month = {1992///}, url = {http://hal.inria.fr/docs/00/07/49/54/PDF/RR-1606.pdf} } @conference {1263, title = {Satellite Image Classification Using a Modified Metropolis Dynamics}, booktitle = {International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year = {1992}, note = {doi: 10.1109/ICASSP.1992.226148}, month = {Mar 1992}, pages = {573 - 576}, publisher = {IEEE Computer Soc. Pr.}, organization = {IEEE Computer Soc. Pr.} }