00706nas a2200217 4500008004100000245005900041210005900100260003400159300000900193100001900202700002000221700002000241700001900261700001700280700001700297700001900314700002200333700002000355700001400375856009900389 2015 eng d00aCollaborative Mobile 3D Reconstruction of Urban Scenes0 aCollaborative Mobile 3D Reconstruction of Urban Scenes aSingaporebSpringercNov 2014 a1-161 aTanacs, Attila1 aMajdik, András1 aHajder, Levente1 aMolnar, Jozsef1 aSanta, Zsolt1 aKato, Zoltan1 aChen, Chu-Song1 aKankanhall, Mohan1 aLai, Shang-Hong1 aHwee, Joo uhttps://www.inf.u-szeged.hu/publication/collaborative-mobile-3d-reconstruction-of-urban-scenes02019nas a2200169 4500008004100000245006500041210006500106260002300171300001400194490000700208520145100215100001901666700002101685700002101706700001701727856010501744 2015 eng d00aEstimation of linear deformations of 2D and 3D fuzzy objects0 aEstimation of linear deformations of 2D and 3D fuzzy objects bElseviercApr 2015 a1387-13990 v483 a
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.
1 aTanacs, Attila1 aLindbald, Joakim1 aSladoje, Nataša1 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/estimation-of-linear-deformations-of-2d-and-3d-fuzzy-objects01343nas a2200157 4500008004100000022001500041245006600056210006600122260002000188300000600208490000700214520082200221100001901043700001701062856010601079 2015 eng d a0162-8828 00aRealigning 2D and 3D Object Fragments without Correspondences0 aRealigning 2D and 3D Object Fragments without Correspondences bIEEEcJune 2015 a10 vpp3 aThis 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.
1 aDomokos, Csaba1 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/realigning-2d-and-3d-object-fragments-without-correspondences00720nas a2200205 4500008004100000245007200041210006900113260003200182300000800214100001900222700002100241700002500262700001700287700002700304700001400331700002100345700001600366700002000382856011200402 2014 eng d00a3D Reconstruction of Planar Patches Seen by Omnidirectional Cameras0 a3D Reconstruction of Planar Patches Seen by Omnidirectional Came aWollongong, AustraliabIEEE a1-81 aMolnar, Jozsef1 aFrohlich, Robert1 aDmitrij, Chetverikov1 aKato, Zoltan1 aBouzerdoum, Abdesselam1 aWang, Lei1 aOgunbona, Philip1 aLi, Wanqing1 aPhung, Son, Lam uhttps://www.inf.u-szeged.hu/publication/3d-reconstruction-of-planar-patches-seen-by-omnidirectional-cameras00615nas a2200169 4500008004100000245006700041210006600108260004600174300000900220100001900229700001500248700001700263700001600280700002400296700001900320856010600339 2014 eng d00a3D Reconstruction of Planar Surface Patches: A Direct Solution0 a3D Reconstruction of Planar Surface Patches A Direct Solution aSingapore, SzingapúrbSpringercNov 2014 a1-8.1 aMolnar, Jozsef1 aHuang, Rui1 aKato, Zoltan1 aZhang, Jian1 aBennamoun, Mohammed1 aPorikli, Fatih uhttps://www.inf.u-szeged.hu/publication/3d-reconstruction-of-planar-surface-patches-a-direct-solution00481nas a2200145 4500008003900000020002200039245004000061210004000101260004400141300001400185100001700199700001700216700002200233856008000255 2014 d a978-4-9906441-0-900aAffine Alignment of Occluded Shapes0 aAffine Alignment of Occluded Shapes aStockholm, SvédországbIEEEcAug 2014 a2155-21601 aSanta, Zsolt1 aKato, Zoltan1 aFelsberg, Michael uhttps://www.inf.u-szeged.hu/publication/affine-alignment-of-occluded-shapes00718nas a2200217 4500008004100000245006200041210006200103260003800165300000800203100001900211700002000230700001900250700001400269700001700283700002700300700001400327700002100341700001600362700002000378856010200398 2014 eng d00aEstablishing Correspondences between Planar Image Patches0 aEstablishing Correspondences between Planar Image Patches aWollongong, AustraliabIEEEc2014 a1-71 aTanacs, Attila1 aMajdik, András1 aMolnar, Jozsef1 aRai, Atul1 aKato, Zoltan1 aBouzerdoum, Abdesselam1 aWang, Lei1 aOgunbona, Philip1 aLi, Wanqing1 aPhung, Son, Lam uhttps://www.inf.u-szeged.hu/publication/establishing-correspondences-between-planar-image-patches00833nas a2200253 4500008004100000022002200041245005600063210005500119260005200174300001200226100002000238700001800258700001700276700001600293700002000309700002800329700002300357700001900380700002700399700001900426700001800445700002400463856009200487 2014 hun d a978-963-473-712-400aKépfeldolgozás a szegedi informatikus-képzésben0 aKépfeldolgozás a szegedi informatikusképzésben aDebrecen, HungarybUniversity of Debrecenc2014 a667-6751 aBalázs, Péter1 aKatona, Endre1 aKato, Zoltan1 aNagy, Antal1 aNémeth, Gábor1 aNyúl, László, Gábor1 aPalágyi, Kálmán1 aTanacs, Attila1 aVarga, László Gábor1 aKunkli, Roland1 aPapp, Ildikó1 aRutkovszky, Edéné uhttps://www.inf.u-szeged.hu/publication/kepfeldolgozas-a-szegedi-informatikus-kepzesben00668nas a2200157 4500008004100000245010600041210007600147260003800223300001400261100001900275700002100294700002100315700001700336700002100353856013600374 2013 eng d00a2D és 3D bináris objektumok lineáris deformáció-becslésének numerikus megoldási lehetőségei0 a2D és 3D bináris objektumok lineáris deformációbecslésének numer aVeszprémbNJSZT-KÉPAFcJan 2013 a526 - 5411 aTanacs, Attila1 aLindblad, Joakim1 aSladoje, Nataša1 aKato, Zoltan1 aCzúni, László uhttps://www.inf.u-szeged.hu/publication/2d-es-3d-binaris-objektumok-linearis-deformacio-becslesenek-numerikus-megoldasi-lehetosegei01349nas a2200133 4500008004100000245007600041210006900117260003900186300001600225520082400241100001701065700001701082856011601099 2013 eng d00aCorrespondence-less non-rigid registration of triangular surface meshes0 aCorrespondenceless nonrigid registration of triangular surface m aPortland, OR, USAbIEEEcJune 2013 a2275 - 22823 a
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'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. © 2013 IEEE.
1 aSanta, Zsolt1 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/correspondence-less-non-rigid-registration-of-triangular-surface-meshes01409nas a2200157 4500008004100000245005000041210005000091260002900141300001000170520094300180100001701123700001701140700001601157700002001173856005801193 2013 eng d00aElastic Registration of 3D Deformable Objects0 aElastic Registration of 3D Deformable Objects aNew YorkbIEEEcNov 2013 a1 - 73 aA novel correspondence-less approach is proposed to find a non-linear aligning transformation between a pair of deformable 3D objects. Herein, we consider a polynomial deformation model, but our framework can be easily adapted to other common deformations. The basic idea of the proposed method is to set up a system of nonlinear equations whose solution directly provides the parameters of the aligning transformation. Each equation is generated by integrating a nonlinear function over the object's domains. Thus the number of equations is determined by the number of adopted nonlinear functions yielding a flexible mechanism to generate sufficiently many equations. While classical approaches would establish correspondences between the shapes, our method works without landmarks. The efficiency of the proposed approach has been demonstrated on a large synthetic dataset as well as in the context of medical image registration.
1 aSanta, Zsolt1 aKato, Zoltan1 aWest, Geoff1 aKövesi, Péter uhttp://www.inf.u-szeged.hu/~kato/papers/dicta2012.pdf01890nas a2200193 4500008004100000245010100041210006900142260002800211300001400239520116200253100001901415700001901434700001701453700002201470700002101492700002001513700002201533856014101555 2013 eng d00aEvaluation of Point Matching Methods for Wide-baseline Stereo Correspondence on Mobile Platforms0 aEvaluation of Point Matching Methods for Widebaseline Stereo Cor aTriestebIEEEcSep 2013 a806 - 8113 aWide-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.
1 aJuhász, Endre1 aTanacs, Attila1 aKato, Zoltan1 aRamponi, Giovanni1 aLončarić, Sven1 aCarini, Alberto1 aEgiazarian, Karen uhttps://www.inf.u-szeged.hu/publication/evaluation-of-point-matching-methods-for-wide-baseline-stereo-correspondence-on-mobile-platforms01221nas a2200145 4500008004100000245006500041210006500106260008200171300001100253520069600264100001700960700001800977700002100995856005901016 2013 eng d00aLinear and nonlinear shape alignment without correspondences0 aLinear and nonlinear shape alignment without correspondences aBerlin; Heidelberg; New York; London; Paris; TokyobSpringer VerlagcFeb 2013 a3 - 173 a
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.
1 aKato, Zoltan1 aRichard, Paul1 aCsurka, Gabriela uhttp://www.inf.u-szeged.hu/~kato/papers/visapp2012.pdf01710nas a2200169 4500008004100000245005300041210005200094260002900146300001200175520116500187100001701352700001701369700002001386700002001406700002101426856009301447 2013 eng d00aPose Estimation of Ad-hoc Mobile Camera Networks0 aPose Estimation of Adhoc Mobile Camera Networks aHobart, TAS bIEEEc2013 a88 - 953 a
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.
1 aSanta, Zsolt1 aKato, Zoltan1 ade Souza, Paulo1 aEngelke, Ulrich1 aRahman, Ashfaqur uhttps://www.inf.u-szeged.hu/publication/pose-estimation-of-ad-hoc-mobile-camera-networks01864nas a2200181 4500008004100000245006400041210006200105260003300167300001400200520125000214100002001464700001701484700001601501700002401517700001901541700002001560856010201580 2013 eng d00aTargetless Calibration of a Lidar - Perspective Camera Pair0 aTargetless Calibration of a Lidar Perspective Camera Pair aSydney, NSW bIEEEcDec 2013 a668 - 6753 a
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.
1 aLevente, Tamás1 aKato, Zoltan1 aZhang, Jian1 aBennamoun, Mohammed1 aSchonfeld, Dan1 aZhang, Zhengyou uhttps://www.inf.u-szeged.hu/publication/targetless-calibration-of-a-lidar-perspective-camera-pair01211nas a2200145 4500008004100000020001400041245009400055210006900149260004300218300001200261490001300273520062800286100001700914856013400931 2013 eng d a1865-092900aA unifying framework for correspondence-less shape alignment and its medical applications0 aunifying framework for correspondenceless shape alignment and it aAllahabad, IndiabSpringercMarch 2013 a40 - 520 v276 CCIS3 a
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. © 2013 Springer-Verlag.
1 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/a-unifying-framework-for-correspondence-less-shape-alignment-and-its-medical-applications01000nas a2200133 4500008004100000245003800041210003800079260001200117520061500129100001900744700001900763700001700782856006700799 2012 eng d00aAffine Registration of 3D Objects0 aAffine Registration of 3D Objects c2012///3 aThis 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.
1 aVarjas, Viktor1 aTanacs, Attila1 aKato, Zoltan uhttp://www.inf.u-szeged.hu/~kato/software/affbin3dregdemo.html00617nam a2200121 4500008004100000245004700041210004700088260003800135520019700173100001700370700002100387856008700408 2012 eng d00aMarkov random fields in image segmentation0 aMarkov random fields in image segmentation aHanover, NHbNow Publishersc20123 aMarkov 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.
1 aKato, Zoltan1 aZerubia, Josiane uhttps://www.inf.u-szeged.hu/publication/markov-random-fields-in-image-segmentation01979nas a2200193 4500008004100000020002300041245008200064210006900146260003500215300001600250520128600266100001801552700001701570700001601587700002201603700001701625700002101642856012201663 2012 eng d a978-1-4673-2216-4 00aA Multi-Layer Phase Field Model for Extracting Multiple Near-Circular Objects0 aMultiLayer Phase Field Model for Extracting Multiple NearCircula aTsukuba, JapanbIEEEcNov 2012 a1427 - 14303 aThis paper proposes a functional that assigns low `energy' to sets of subsets of the image domain consisting of a number of possibly overlapping near-circular regions of approximately a given radius: a `gas of circles'. The model can be used as a prior for object extraction whenever the objects conform to the `gas of circles' 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.
1 aMolnar, Csaba1 aKato, Zoltan1 aJermyn, Ian1 aEklundh, Jan-Olof1 aOhta, Yuichi1 aTanimoto, Steven uhttps://www.inf.u-szeged.hu/publication/a-multi-layer-phase-field-model-for-extracting-multiple-near-circular-objects01673nas a2200169 4500008004100000020001400041245005700055210005700112260001500169300001400184490000700198520116800205100001901373700001901392700001701411856007501428 2012 eng d a0162-882800aNonlinear Shape Registration without Correspondences0 aNonlinear Shape Registration without Correspondences bIEEEc2012 a943 - 9580 v343 a
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.
1 aDomokos, Csaba1 aNemeth, Jozsef1 aKato, Zoltan uhttp://www.inf.u-szeged.hu/~kato/papers/TPAMI-2010-03-0146.R2_Kato.pdf02317nas a2200169 4500008004100000020002200041245009300063210006900156260007100225300001400296520160900310100002601919700001801945700001701963700003401980856013302014 2012 eng d a978-1-4419-6189-100aParametric Stochastic Modeling for Color Image Segmentation and Texture Characterization0 aParametric Stochastic Modeling for Color Image Segmentation and aBerlin; Heidelberg; New York; London; Paris; TokyobSpringerc2012 a279 - 3253 a
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.
1 aQazi, Imtnan-Ul-Haque1 aAlata, Oliver1 aKato, Zoltan1 aFernandez-Maloigne, Christine uhttps://www.inf.u-szeged.hu/publication/parametric-stochastic-modeling-for-color-image-segmentation-and-texture-characterization01249nas a2200181 4500008004100000020002300041245005600064210005600120260003500176300001100211520065300222100001900875700001700894700002200911700001700933700002100950856009600971 2012 eng d a978-1-4673-2216-4 00aSimultaneous Affine Registration of Multiple Shapes0 aSimultaneous Affine Registration of Multiple Shapes aTsukuba, JapanbIEEEcNov 2012 a9 - 123 a
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.
1 aDomokos, Csaba1 aKato, Zoltan1 aEklundh, Jan-Olof1 aOhta, Yuichi1 aTanimoto, Steven uhttps://www.inf.u-szeged.hu/publication/simultaneous-affine-registration-of-multiple-shapes03162nas a2200265 4500008004100000020002300041245008800064210006900152260003500221300001600256520231800272100001802590700001702608700001802625700001902643700001902662700001902681700001802700700002102718700002402739700002202763700001702785700002102802856007302823 2012 eng d a978-1-4673-2216-4 00aSpectral clustering to model deformations for fast multimodal prostate registration0 aSpectral clustering to model deformations for fast multimodal pr aTsukuba, JapanbIEEEcNov 2012 a2622 - 26253 a
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 ± 0.025, 95% Hausdorff distance of 5.14 ± 3.67 mm and target registration error of 2.44 ± 1.17 mm are obtained by our method with a speed-up in computation time by 98% when compared to Mitra et al. [7].
1 aMitra, Jhimli1 aKato, Zoltan1 aGhose, Soumya1 aSidibe, Desire1 aMartí, Robert1 aLladó, Xavier1 aArnau, Oliver1 aVilanova, Joan C1 aMeriaudeau, Fabrice1 aEklundh, Jan-Olof1 aOhta, Yuichi1 aTanimoto, Steven uhttp://hal.archives-ouvertes.fr/docs/00/71/09/43/PDF/ICPR_Jhimli.pdf02351nas a2200253 4500008004100000020001400041245008300055210006900138260001300207300001600220490000700236520154200243100001801785700001701803700001901820700001801839700001901857700001901876700001801895700002101913700001701934700002401951856012201975 2012 eng d a1361-841500aA spline-based non-linear diffeomorphism for multimodal prostate registration.0 asplinebased nonlinear diffeomorphism for multimodal prostate reg cAug 2012 a1259 - 12790 v163 a
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.
1 aMitra, Jhimli1 aKato, Zoltan1 aMartí, Robert1 aArnau, Oliver1 aLladó, Xavier1 aSidibe, Desire1 aGhose, Soumya1 aVilanova, Joan C1 aComet, Josep1 aMeriaudeau, Fabrice uhttps://www.inf.u-szeged.hu/publication/a-spline-based-non-linear-diffeomorphism-for-multimodal-prostate-registration01359nas a2200145 4500008004100000020002200041245007200063210006900135260004900204300001400253520079400267100001701061700002301078856011201101 2012 eng d a978-3-642-31294-600aA Unifying Framework for Correspondence-less Linear Shape Alignment0 aUnifying Framework for Correspondenceless Linear Shape Alignment aAveiro, PortugalbSpringer VerlagcJune 2012 a277 - 2843 aWe 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.
1 aKato, Zoltan1 aCampilho, Aurélio uhttps://www.inf.u-szeged.hu/publication/a-unifying-framework-for-correspondence-less-linear-shape-alignment01488nas a2200145 4500008004100000020002300041245006700064210006400131260003800195300001400233520099700247100001701244700001701261856006401278 2012 eng d a978-1-4673-5187-4 00aA Unifying Framework for Non-linear Registration of 3D Objects0 aUnifying Framework for Nonlinear Registration of 3D Objects aKosice, Slovakia bIEEEcDec 2012 a547 - 5523 a
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'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.
1 aSanta, Zsolt1 aKato, Zoltan uhttp://www.inf.u-szeged.hu/~kato/papers/coginfocomm2012.pdf00572nas a2200169 4500008004100000245005500041210005500096260002800151300001400179100001900193700002100212700002100233700001700254700001700271700002300288856009100311 2011 eng d00a3D objektumok lineáris deformációinak becslése0 a3D objektumok lineáris deformációinak becslése aSzegedbNJSZTcJan 2011 a471 - 4801 aTanacs, Attila1 aLindblad, Joakim1 aSladoje, Nataša1 aKato, Zoltan1 aKato, Zoltan1 aPalágyi, Kálmán uhttps://www.inf.u-szeged.hu/publication/3d-objektumok-linearis-deformacioinak-becslese00524nas a2200145 4500008004100000245008900041210007300130260002800203300001400231100001900245700001700264700001700281700002300298856005700321 2011 eng d00aAffin Puzzle: Deformált objektumdarabok helyreállítása megfeleltetések nélkül0 aAffin Puzzle Deformált objektumdarabok helyreállítása megfelelte aSzegedbNJSZTcJan 2011 a206 - 2201 aDomokos, Csaba1 aKato, Zoltan1 aKato, Zoltan1 aPalágyi, Kálmán uhttp://www.inf.u-szeged.hu/kepaf2011/pdfs/S05_03.pdf00582nas a2200145 4500008004100000245008300041210007500124260002800199300001400227100001800241700002000259700001700279700002300296856011700319 2011 eng d00aBináris tomográfiai rekonstrukció objektum alapú evolúciós algoritmussal0 aBináris tomográfiai rekonstrukció objektum alapú evolúciós algor aSzegedbNJSZTcJan 2011 a117 - 1271 aGara, Mihály1 aBalázs, Péter1 aKato, Zoltan1 aPalágyi, Kálmán uhttps://www.inf.u-szeged.hu/publication/binaris-tomografiai-rekonstrukcio-objektum-alapu-evolucios-algoritmussal00689nas a2200205 4500008004100000245009700041210008000138260002800218300001400246100001700260700001700277700001600294700001700310700001900327700002000346700002000366700001700386700002300403856005700426 2011 eng d00aÉlősejt szegmentálása gráfvágás segítségével fluoreszcenciás mikroszkóp képeken0 aÉlősejt szegmentálása gráfvágás segítségével fluoreszcenciás mik aSzegedbNJSZTcJan 2011 a319 - 3281 aLesko, Milan1 aKato, Zoltan1 aNagy, Antal1 aGombos, Imre1 aTörök, Zsolt1 aVígh, László1 aVígh, László1 aKato, Zoltan1 aPalágyi, Kálmán uhttp://www.inf.u-szeged.hu/kepaf2011/pdfs/S08_02.pdf01274nas a2200205 4500008004100000020002300041245007300064210006900137260002900206300001400235520058300249100001900832700001700851700002000868700001900888700001400907700001900921700001500940856011300955 2011 eng d a978-1-4244-9351-7 00aFast linear registration of 3D objects segmented from medical images0 aFast linear registration of 3D objects segmented from medical im aShanghaibIEEEcOct 2011 a294 - 2983 a
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. © 2011 IEEE.
1 aTanacs, Attila1 aKato, Zoltan1 aDing, Yongsheng1 aPeng, Yonghong1 aShi, Riyi1 aHao, Kuangrong1 aWang, Lipo uhttps://www.inf.u-szeged.hu/publication/fast-linear-registration-of-3d-objects-segmented-from-medical-images00520nas a2200157 4500008004100000245006000041210006000101260002800161300001400189100001900203700002000222700002300242700001700265700002300282856005700305 2011 eng d00aIterációnkénti simítással kombinált vékonyítás0 aIterációnkénti simítással kombinált vékonyítás aSzegedbNJSZTcJan 2011 a174 - 1891 aKardos, Péter1 aNémeth, Gábor1 aPalágyi, Kálmán1 aKato, Zoltan1 aPalágyi, Kálmán uhttp://www.inf.u-szeged.hu/kepaf2011/pdfs/S05_01.pdf00559nas a2200145 4500008004100000245007200041210007200113260002800185300001400213100002000227700002000247700001700267700002300284856010600307 2011 eng d00aMediánszűrés alkalmazása algebrai rekonstrukciós módszerekben0 aMediánszűrés alkalmazása algebrai rekonstrukciós módszerekben aSzegedbNJSZTcJan 2011 a106 - 1161 aHantos, Norbert1 aBalázs, Péter1 aKato, Zoltan1 aPalágyi, Kálmán uhttps://www.inf.u-szeged.hu/publication/medianszures-alkalmazasa-algebrai-rekonstrukcios-modszerekben01714nas a2200217 4500008004100000020002200041245011700063210006900180260004600249300001400295520093900309100001901248700001701267700001601284700002501300700002201325700001701347700002101364700002301385856008801408 2011 eng d a978-3-642-23686-000aA Multi-Layer 'Gas of Circles' Markov Random Field Model for the Extraction of Overlapping Near-Circular Objects0 aMultiLayer Gas of Circles Markov Random Field Model for the Extr aGhent, BelgiumbSpringer-VerlagcAug 2011 a171 - 1823 aWe 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.
1 aNemeth, Jozsef1 aKato, Zoltan1 aJermyn, Ian1 aBlanc-Talon, Jacques1 aPhilips, Wilfried1 aPopescu, Dan1 aScheunders, Paul1 aKleihorst, Richard uhttp://www.inf.u-szeged.hu/ipcg/publications/Year/2011.complete.xml#Nemeth-etal201102227nas a2200217 4500008004100000020002300041245007800064210006900142260003200211300001200243520148200255100001801737700001701755700001901772700001801791700001901809700001801828700002101846700002401867856011801891 2011 eng d a978-1-4577-2006-2 00aA non-linear diffeomorphic framework for prostate multimodal registration0 anonlinear diffeomorphic framework for prostate multimodal regist aNoosa, QLD bIEEEcDec 2011 a31 - 363 aThis 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 ± 0.004, average 95% Hausdorff distance of 1.54 ± 0.46 mm and mean target registration and target localization errors of 1.90±1.27 mm and 0.15 ± 0.12 mm respectively. © 2011 IEEE.
1 aMitra, Jhimli1 aKato, Zoltan1 aMartí, Robert1 aArnau, Oliver1 aLladó, Xavier1 aGhose, Soumya1 aVilanova, Joan C1 aMeriaudeau, Fabrice uhttps://www.inf.u-szeged.hu/publication/a-non-linear-diffeomorphic-framework-for-prostate-multimodal-registration00956nas a2200145 4500008004100000245005700041210005700098260001200155520048800167100002900655700001900684700001900703700001700722856007100739 2011 eng d00aNonlinear Shape Registration without Correspondences0 aNonlinear Shape Registration without Correspondences c2011///3 aThis 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.
1 aTörök, Zoltán Kornél1 aDomokos, Csaba1 aNemeth, Jozsef1 aKato, Zoltan uhttp://www.inf.u-szeged.hu/~kato/software/planarhombinregdemo.html00345nam a2200109 4500008004100000245002900041210002900070260003500099100001700134700002100151856006300172 2011 hun d00aSzámítógépes látás0 aSzámítógépes látás aBudapestbTypotex Kiadóc20111 aKato, Zoltan1 aCzúni, László uhttps://www.inf.u-szeged.hu/publication/szamitogepes-latas00578nas a2200157 4500008004100000245010100041210007700142260002800219300001400247100002000261700001900281700002300300700001700323700002300340856005700363 2011 hun d00aA topológia-megőrzés elegendő feltételein alapuló 3D párhuzamos vékonyító algoritmusok0 atopológiamegőrzés elegendő feltételein alapuló 3D párhuzamos vék aSzegedbNJSZTcJan 2011 a190 - 2051 aNémeth, Gábor1 aKardos, Péter1 aPalágyi, Kálmán1 aKato, Zoltan1 aPalágyi, Kálmán uhttp://www.inf.u-szeged.hu/kepaf2011/pdfs/S05_02.pdf00552nas a2200157 4500008004100000245005900041210005900100260002800159300001300187100002700200700002000227700001600247700001700263700002300280856009100303 2011 hun d00aVetületi irányfüggőség a bináris tomográfiában0 aVetületi irányfüggőség a bináris tomográfiában aSzegedbNJSZTcJan 2011 a92 - 1051 aVarga, László Gábor1 aBalázs, Péter1 aNagy, Antal1 aKato, Zoltan1 aPalágyi, Kálmán uhttps://www.inf.u-szeged.hu/publication/vetuleti-iranyfuggoseg-a-binaris-tomografiaban01393nas a2200193 4500008004100000020002200041022001400063245008000077210006900157260003800226300001400264520070300278100001900981700001701000700002301017700002001040700002001060856011901080 2010 eng d a978-3-642-15551-2 a0302-974300aAffine puzzle: Realigning deformed object fragments without correspondences0 aAffine puzzle Realigning deformed object fragments without corre aCrete, GreecebSpringercSep 2010 a777 - 7903 aThis 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. © 2010 Springer-Verlag.
1 aDomokos, Csaba1 aKato, Zoltan1 aDaniilidis, Kostas1 aMaragos, Petros1 aParagios, Nikos uhttps://www.inf.u-szeged.hu/publication/affine-puzzle-realigning-deformed-object-fragments-without-correspondences01140nas a2200157 4500008004100000245005200041210005200093260004100145300001400186520061200200100001900812700002100831700002100852700001700873856009200890 2010 eng d00aEstimation of linear deformations of 3D objects0 aEstimation of linear deformations of 3D objects aHong Kong, Hong KongbIEEEcSep 2010 a153 - 1563 aWe 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. © 2010 IEEE.
1 aTanacs, Attila1 aLindblad, Joakim1 aSladoje, Nataša1 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/estimation-of-linear-deformations-of-3d-objects01157nas a2200229 4500008004100000020002300041022001500064245006800079210006800147260003700215300001600252520040600268100001700674700001700691700001600708700001700724700001900741700002000760700002000780700001700800856011000817 2010 eng d a978-1-4244-7542-1 a1051-4651 00aLive cell segmentation in fluorescence microscopy via graph cut0 aLive cell segmentation in fluorescence microscopy via graph cut aIstanbul, TurkeybIEEEcAug 2010 a1485 - 14883 aWe 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. © 2010 IEEE.
1 aLesko, Milan1 aKato, Zoltan1 aNagy, Antal1 aGombos, Imre1 aTörök, Zsolt1 aVígh, László1 aVígh, László1 aErcil, Aytul uhttps://www.inf.u-szeged.hu/publication/live-cell-segmentation-in-fluorescence-microscopy-via-graph-cut-000511nas a2200145 4500008004100000020001400041245006600055210006600121260001500187300001400202490000700216100001900223700001700242856010600259 2010 eng d a0031-320300aParametric estimation of affine deformations of planar shapes0 aParametric estimation of affine deformations of planar shapes cMarch 2010 a569 - 5780 v431 aDomokos, Csaba1 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/parametric-estimation-of-affine-deformations-of-planar-shapes00613nas a2200157 4500008004100000245007500041210006900116260005200185300000700237100002000244700001700264700002000281700002000301700002100321856011300342 2010 eng d00aSITIS 2010: Track SIT editorial message: Signal and Image Technologies0 aSITIS 2010 Track SIT editorial message Signal and Image Technolo aKuala LumpurbIEEE Computer Society Pressc2010 aXV1 aDipanda, Albert1 aKato, Zoltan1 aDipanda, Albert1 aChbeir, Richard1 aYetongnon, Kokou uhttps://www.inf.u-szeged.hu/publication/sitis-2010-track-sit-editorial-message-signal-and-image-technologies01287nas a2200157 4500008004100000020002300041022001500064245006000079210005900139260003300198300001400231520074900245100001900994700001701013856009901030 2009 eng d a978-1-4244-5653-6 a1522-4880 00aAffine alignment of compound objects: A direct approach0 aAffine alignment of compound objects A direct approach aCairo, EgyptbIEEEcNov 2009 a169 - 1723 aA 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. ©2009 IEEE.
1 aDomokos, Csaba1 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/affine-alignment-of-compound-objects-a-direct-approach00675nas a2200133 4500008004100000245004100041210004100082260001200123520028700135100001800422700001900440700001700459856006500476 2009 eng d00aAffine Registration of Planar Shapes0 aAffine Registration of Planar Shapes c2009///3 aThis 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.
1 aKatona, Zsolt1 aDomokos, Csaba1 aKato, Zoltan uhttp://www.inf.u-szeged.hu/~kato/software/affbinregdemo.html01146nas a2200181 4500008004100000020001400041245009500055210006900150260001500219300001600234490000700250520049500257100001900752700002000771700001700791700002100808856013500829 2009 eng d a1057-714900aDetection of Object Motion Regions in Aerial Image Pairs with a Multilayer Markovian Model0 aDetection of Object Motion Regions in Aerial Image Pairs with a bIEEEc2009 a2303 - 23150 v183 aWe 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.
1 aBenedek, Csaba1 aSziranyi, Tamas1 aKato, Zoltan1 aZerubia, Josiane uhttps://www.inf.u-szeged.hu/publication/detection-of-object-motion-regions-in-aerial-image-pairs-with-a-multilayer-markovian-model00652nas a2200169 4500008004100000020001400041245010700055210006900162260001200231300001400243490000700257100001900264700001600283700001700299700002100316856014500337 2009 eng d a0031-320300aA higher-order active contour model of a 'gas of circles' and its application to tree crown extraction0 ahigherorder active contour model of a gas of circles and its app c2009/// a699 - 7090 v421 aHorvath, Peter1 aJermyn, Ian1 aKato, Zoltan1 aZerubia, Josiane uhttps://www.inf.u-szeged.hu/publication/a-higher-order-active-contour-model-of-a-gas-of-circles-and-its-application-to-tree-crown-extraction00563nas a2200157 4500008004100000245007200041210007200113260003300185300001000218100002300228700001600251700001700267700002500284700002000309856007600329 2009 hun d00aKör alakú objektumok szegmentálása Markov mező segítségével0 aKör alakú objektumok szegmentálása Markov mező segítségével aBudapestbAkaprintcJan 2009 a1 - 91 aBlaskovics, Tamás1 aJermyn, Ian1 aKato, Zoltan1 aChetverikov, Dmitrij1 aSziranyi, Tamas uhttp://vision.sztaki.hu/~kepaf/kepaf2009_CD/files/116-4-MRFCircle08.pdf01105nas a2200157 4500008004100000020002300041245006800064210006500132260003300197300001600230520053700246100002300783700001700806700001600823856010800839 2009 eng d a978-1-4244-5653-6 00aA Markov random field model for extracting near-circular shapes0 aMarkov random field model for extracting nearcircular shapes aCairo, EgyptbIEEEcNov 2009 a1073 - 10763 aWe 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 'gas of circles' phase field model in a principled way, thereby creating an 'equivalent'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. ©2009 IEEE.
1 aBlaskovics, Tamás1 aKato, Zoltan1 aJermyn, Ian uhttps://www.inf.u-szeged.hu/publication/a-markov-random-field-model-for-extracting-near-circular-shapes01317nas a2200157 4500008004100000020002300041245004400064210004400108260003300152300001600185520081900201100001901020700001901039700001701058856008401075 2009 eng d a978-1-4244-5653-6 00aNonlinear registration of binary shapes0 aNonlinear registration of binary shapes aCairo, EgyptbIEEEcNov 2009 a1101 - 11043 aA 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. ©2009 IEEE.
1 aNemeth, Jozsef1 aDomokos, Csaba1 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/nonlinear-registration-of-binary-shapes01494nas a2200205 4500008004100000245005100041210005100092260004500143300001400188520082800202100001901030700001901049700002101068700002101089700001701110700002401127700002601151700002001177856009101197 2009 eng d00aRecovering affine deformations of fuzzy shapes0 aRecovering affine deformations of fuzzy shapes aOslo, NorwaybSpringer-VerlagcJune 2009 a735 - 7443 aFuzzy 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. © 2009 Springer Berlin Heidelberg.
1 aTanacs, Attila1 aDomokos, Csaba1 aSladoje, Nataša1 aLindblad, Joakim1 aKato, Zoltan1 aSalberg, Arnt-Borre1 aHardeberg, Jon, Yngve1 aJenssen, Robert uhttps://www.inf.u-szeged.hu/publication/recovering-affine-deformations-of-fuzzy-shapes01420nas a2200157 4500008004100000245005300041210005300094260001800147300001600165520092800181100001901109700001901128700001701147700000501164856009301169 2009 eng d00aRecovering planar homographies between 2D shapes0 aRecovering planar homographies between 2D shapes bIEEEc2009/// a2170 - 21763 aImages 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. ©2009 IEEE.
1 aNemeth, Jozsef1 aDomokos, Csaba1 aKato, Zoltan1 a uhttps://www.inf.u-szeged.hu/publication/recovering-planar-homographies-between-2d-shapes00586nas a2200145 4500008004100000245008400041210007600125260003300201300001000234100001900244700001700263700002500280700002000305856011500325 2009 eng d00aSíkbeli alakzatok regisztrációja kovariáns függvények felhasználásával0 aSíkbeli alakzatok regisztrációja kovariáns függvények felhasznál aBudapestbAkaprintcJan 2009 a1 - 81 aDomokos, Csaba1 aKato, Zoltan1 aChetverikov, Dmitrij1 aSziranyi, Tamas uhttps://www.inf.u-szeged.hu/publication/sikbeli-alakzatok-regisztracioja-kovarians-fuggvenyek-felhasznalasaval00568nas a2200157 4500008004100000245006400041210006400105260003300169300001000202100001900212700001900231700001700250700002500267700002000292856009800312 2009 eng d00aSíkhomográfia paramétereinek becslése bináris képeken0 aSíkhomográfia paramétereinek becslése bináris képeken aBudapestbAkaprintcJan 2009 a1 - 81 aNemeth, Jozsef1 aDomokos, Csaba1 aKato, Zoltan1 aChetverikov, Dmitrij1 aSziranyi, Tamas uhttps://www.inf.u-szeged.hu/publication/sikhomografia-parametereinek-becslese-binaris-kepeken01168nas a2200121 4500008004100000245006500041210006500106260001200171520076400183100001800947700001700965856006400982 2009 eng d00aSupervised Color Image Segmentation in a Markovian Framework0 aSupervised Color Image Segmentation in a Markovian Framework c2009///3 aThis 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.
1 aGara, Mihály1 aKato, Zoltan uhttp://www.inf.u-szeged.hu/~kato/software/colormrfdemo.html01516nas a2200157 4500008004100000020002200041245006500063210006500128260005200193300001400245520093500259100001901194700001701213700002301230856010501253 2008 eng d a978-3-540-69811-100aBinary image registration using covariant gaussian densities0 aBinary image registration using covariant gaussian densities aPóvoa de Varzim, PortugalbSpringercJune 2008 a455 - 4643 aWe 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. © 2008 Springer-Verlag Berlin Heidelberg.
1 aDomokos, Csaba1 aKato, Zoltan1 aCampilho, Aurélio uhttps://www.inf.u-szeged.hu/publication/binary-image-registration-using-covariant-gaussian-densities01602nas a2200229 4500008004100000245006200041210006000103260005800163520086900221100002001090700002401110700001801134700001701152700001801169700001601187700002801203700002301231700001901254700001901273700002001292856006001312 2008 eng d00aA képfeldolgozás kutatása a Szegedi Tudományegyetemen0 aképfeldolgozás kutatása a Szegedi Tudományegyetemen aDebrecenbDebreceni Egyetem Informatikai Karc2008///3 aA digitális képfeldolgozás kutatásának a Szegedi TudományegyetemTermészettudományi és Informatikai Karán, az Informatikai Tanszékcsoport Képfeldolgozás és Számítógépes Grafika Tanszékén közel négy évtizedes hagyománya van. A Tanszék valamennyi munkatársa nemzetközileg elismert kutatómunkát folytat, melyet már több száz rangos publikáció fémjelez. Számos, a képfeldolgozás kutatásában vezető egyetemmel és kutatóintézettel építettünk ki szoros kapcsolatot és folytattunk eredményes kutatómunkát, aktív résztvevői vagyunk a hazai és a nemzetközi tudományos közéletnek. A legfontosabb, jelenleg is folyó kutatásaink: orvosi képek feldolgozása, diszkrét tomográfia, képszegmentálás, térinformatika, távérzékelés, képregisztráció, vázkijelölés, műtéti tervezés. 1 aBalázs, Péter1 aErdőhelyi, Balázs1 aKatona, Endre1 aKato, Zoltan1 aMáté, Eörs1 aNagy, Antal1 aNyúl, László, Gábor1 aPalágyi, Kálmán1 aTanacs, Attila1 aPethő, Attila1 aHerdon, Miklós uhttp://www.agr.unideb.hu/if2008/kiadvany/papers/E62.pdf01341nas a2200169 4500008004100000020002300041022001500064245006600079210006600145260004100211300001400252520074100266100001901007700001701026700002201043856010601065 2008 eng d a978-1-4244-1483-3 a1520-6149 00aParametric estimation of affine deformations of binary images0 aParametric estimation of affine deformations of binary images aLas Vegas, NV, USAbIEEEcMarch 2008 a889 - 8923 aWe 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. ©2008 IEEE.
1 aDomokos, Csaba1 aKato, Zoltan1 aFrancos, Joseph M uhttps://www.inf.u-szeged.hu/publication/parametric-estimation-of-affine-deformations-of-binary-images00493nas a2200133 4500008004100000020001400041245006700055210006700122260002500189300001400214490000700228100001700235856010700252 2008 eng d a0262-885600aSegmentation of color images via reversible jump MCMC sampling0 aSegmentation of color images via reversible jump MCMC sampling bElseviercMarch 2008 a361 - 3710 v261 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/segmentation-of-color-images-via-reversible-jump-mcmc-sampling00715nas a2200169 4500008004100000245009900041210007600140260007300216300001400289100001900303700001600322700001700338700002100355700002000376700002000396856012900416 2007 eng d00aKör alakú objektumok szegmentálása magasabb rendű aktív kontúr modellek segítségével0 aKör alakú objektumok szegmentálása magasabb rendű aktív kontúr m aDebrecenbKépfeldolgozók és Alakfelismerők TársaságacJan 2007 a133 - 1401 aHorvath, Peter1 aJermyn, Ian1 aKato, Zoltan1 aZerubia, Josiane1 aFazekas, Attila1 aHajdú, András uhttps://www.inf.u-szeged.hu/publication/kor-alaku-objektumok-szegmentalasa-magasabb-rendu-aktiv-kontur-modellek-segitsegevel00416nas a2200097 4500008004100000245007100041210006900112260000900181100001700190856011100207 2007 eng d00aMarkovian Image Models and their Application in Image Segmentation0 aMarkovian Image Models and their Application in Image Segmentati c20071 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/markovian-image-models-and-their-application-in-image-segmentation00663nas a2200157 4500008004100000245008500041210006900126260007300195300001400268100001900282700001700301700002200318700002000340700002000360856012500380 2007 eng d00aParametric Estimation of Two-Dimensional Affine Transformations of Binary Images0 aParametric Estimation of TwoDimensional Affine Transformations o aDebrecenbKépfeldolgozók és Alakfelismerők TársaságacJan 2007 a257 - 2651 aDomokos, Csaba1 aKato, Zoltan1 aFrancos, Joseph M1 aFazekas, Attila1 aHajdú, András uhttps://www.inf.u-szeged.hu/publication/parametric-estimation-of-two-dimensional-affine-transformations-of-binary-images00523nas a2200133 4500008004100000245007500041210006900116260001200185100001900197700002000216700001700236700002100253856011500274 2007 eng d00aA Three-layer MRF model for Object Motion Detection in Airborne Images0 aThreelayer MRF model for Object Motion Detection in Airborne Ima c2007///1 aBenedek, Csaba1 aSziranyi, Tamas1 aKato, Zoltan1 aZerubia, Josiane uhttps://www.inf.u-szeged.hu/publication/a-three-layer-mrf-model-for-object-motion-detection-in-airborne-images01532nas a2200169 4500008004100000245005900041210005600100260001800156300001400174520098900188100001901177700001601196700001701212700002101229700001301250856009901263 2006 eng d00aA higher-order active contour model for tree detection0 ahigherorder active contour model for tree detection bIEEEc2006/// a130 - 1333 aWe present a model of a 'gas of circles', 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 'higher order active contours' (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. © 2006 IEEE.
1 aHorvath, Peter1 aJermyn, Ian1 aKato, Zoltan1 aZerubia, Josiane1 aTang, YY uhttps://www.inf.u-szeged.hu/publication/a-higher-order-active-contour-model-for-tree-detection01535nas a2200169 4500008003900000245005900039210005600098260003300154300001400187490000600201520098600207100001901193700001601212700001701228700002101245856009901266 2006 d00aA Higher-Order Active Contour Model for Tree Detection0 aHigherOrder Active Contour Model for Tree Detection aHong Kong, ChinabIAPRc2006 a130–1330 v23 aWe present a model of a 'gas of circles', 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 'higher order active contours' (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.
1 aHorvath, Peter1 aJermyn, Ian1 aKato, Zoltan1 aZerubia, Josiane uhttps://www.inf.u-szeged.hu/publication/a-higher-order-active-contour-model-for-tree-detection00583nas a2200133 4500008004100000245010700041210006900148260001200217100001900229700001600248700001700264700002100281856014700302 2006 eng d00aA Higher-Order Active Contour Model of a `Gas of Circles' and its Application to Tree Crown Extraction0 aHigherOrder Active Contour Model of a Gas of Circles and its App c2006///1 aHorvath, Peter1 aJermyn, Ian1 aKato, Zoltan1 aZerubia, Josiane uhttps://www.inf.u-szeged.hu/publication/a-higher-order-active-contour-model-of-a-gas-of-circles-and-its-application-to-tree-crown-extraction-000722nas a2200169 4500008004100000245011200041210006900153260005900222300001400281100001900295700001600314700001700330700002100347700001600368700001800384856015000402 2006 eng d00aAn Improved `Gas of Circles' Higher-Order Active Contour Model and its Application to Tree Crown Extraction0 aImproved Gas of Circles HigherOrder Active Contour Model and its aBerlin; Heidelberg; New YorkbSpringer Verlagc2006/// a152 - 1611 aHorvath, Peter1 aJermyn, Ian1 aKato, Zoltan1 aZerubia, Josiane1 aKalra, Prem1 aPeleg, Shmuel uhttps://www.inf.u-szeged.hu/publication/an-improved-gas-of-circles-higher-order-active-contour-model-and-its-application-to-tree-crown-extraction00537nas a2200145 4500008004100000020001400041245007700055210006900132260001200201300001600213490000700229100001700236700002100253856011700274 2006 eng d a0262-885600aA Markov random field image segmentation model for color textured images0 aMarkov random field image segmentation model for color textured c2006/// a1103 - 11140 v241 aKato, Zoltan1 aPong, Ting Chuen uhttps://www.inf.u-szeged.hu/publication/a-markov-random-field-image-segmentation-model-for-color-textured-images00545nas a2200157 4500008004100000245009300041210006900134260003000203300002000233100001900253700002000272700001700292700002100309700000500330856005200335 2006 eng d00aA multi-layer MRF model for object-motion detection in unregistered airborne image-pairs0 amultilayer MRF model for objectmotion detection in unregistered aPiscatawaybIEEEc2006/// aVI-141 - VI-1441 aBenedek, Csaba1 aSziranyi, Tamas1 aKato, Zoltan1 aZerubia, Josiane1 a uhttp://www.icip2007.org/Papers/AcceptedList.asp00539nas a2200157 4500008004100000245005800041210005500099260002900154300001400183100001700197700002100214700001900235700001500254700001400269856009800283 2006 eng d00aA multi-layer MRF model for video object segmentation0 amultilayer MRF model for video object segmentation bSpringer Verlagc2006/// a953 - 9621 aKato, Zoltan1 aPong, Ting Chuen1 aNarayanan, P J1 aNayar, S K1 aShum, H Y uhttps://www.inf.u-szeged.hu/publication/a-multi-layer-mrf-model-for-video-object-segmentation00628nas a2200193 4500008004100000245005100041210005100092260002500143300001400168100001900182700002300201700001600224700002100240700001700261700002500278700002100303700001900324856009100343 2005 eng d00aShape Moments for Region Based Active Contours0 aShape Moments for Region Based Active Contours aViennabOCGc2005/// a187 - 1941 aHorvath, Peter1 aBhattacharya, Avik1 aJermyn, Ian1 aZerubia, Josiane1 aKato, Zoltan1 aChetverikov, Dmitrij1 aCzúni, László1 aVincze, Markus uhttps://www.inf.u-szeged.hu/publication/shape-moments-for-region-based-active-contours01500nas a2200121 4500008004100000245006100041210006100102260001200163520110700175100002001282700001701302856005901319 2005 eng d00aSupervised Image Segmentation Using Markov Random Fields0 aSupervised Image Segmentation Using Markov Random Fields c2005///3 aThis is the sample implementation of a Markov random field based image segmentation algorithm described in the following papers: Mark Berthod, Zoltan Kato, Shan Yu, and Josiane Zerubia. Bayesian Image Classification Using Markov Random Fields. Image and Vision Computing, 14:285--295, 1996. Keyword(s): Bayesian image classification, Markov random fields, Optimisation. Zoltan Kato, Josiane Zerubia, and Mark Berthod. Satellite Image Classification Using a Modified Metropolis Dynamics. In Proceedings of International Conference on Acoustics, Speech and Signal Processing, volume 3, San-Francisco, California, USA, pages 573-576, March 1992. IEEE. Zoltan Kato. Modélisations markoviennes multirésolutions en vision par ordinateur. Application a` la segmentation d'images SPOT. PhD Thesis, INRIA, Sophia Antipolis, France, December 1994. Note: Available in French (follow the URL link) and English. Keyword(s): computer vision, early vision, Markovian model, multiscale model, hierarchical model, parallel combinatorial optimization algorithm, multi-temperature annealing, parameter estimation. 1 aGradwohl, Csaba1 aKato, Zoltan uhttp://www.inf.u-szeged.hu/~kato/software/mrfdemo.html00570nas a2200157 4500008004100000245006300041210006300104260002500167300001400192100001700206700002100223700002500244700002100269700001900290856010300309 2005 eng d00aVideo Object Segmentation Using a Multicue Markovian Model0 aVideo Object Segmentation Using a Multicue Markovian Model aViennabOCGc2005/// a111 - 1181 aKato, Zoltan1 aPong, Ting Chuen1 aChetverikov, Dmitrij1 aCzúni, László1 aVincze, Markus uhttps://www.inf.u-szeged.hu/publication/video-object-segmentation-using-a-multicue-markovian-model00490nas a2200121 4500008004100000245007000041210006900111260002900180300001400209100001900223700001700242856010900259 2004 eng d00aColor, Texture and Motion Segmentation Using Gradient Vector Flow0 aColor Texture and Motion Segmentation Using Gradient Vector Flow aMiskolctapolcacJan 2004 a131 - 1371 aHorvath, Peter1 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/color-texture-and-motion-segmentation-using-gradient-vector-flow00534nas a2200133 4500008004100000245007400041210006900115260002900184300001400213100001700227700002100244700002100265856011400286 2004 eng d00aColor textured image segmentation using a multi-layer Markovian model0 aColor textured image segmentation using a multilayer Markovian m aMiskolctapolcacJan 2004 a152 - 1581 aKato, Zoltan1 aPong, Ting Chuen1 aQiang, Song, Guo uhttps://www.inf.u-szeged.hu/publication/color-textured-image-segmentation-using-a-multi-layer-markovian-model00483nas a2200121 4500008004100000245006700041210006700108260002900175300001400204100001900218700001700237856010700254 2004 eng d00aOptical Flow Computation Using an Energy Minimization Approach0 aOptical Flow Computation Using an Energy Minimization Approach aMiskolctapolcacJan 2004 a125 - 1301 aHorvath, Peter1 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/optical-flow-computation-using-an-energy-minimization-approach00557nas a2200145 4500008004100000245014700041210006900188260001800257300001200275100001700287700001900304700001800323700001500341856005500356 2004 eng d00aReversible Jump Markov Chain Monte Carlo for Unsupervised MRF Color Image SegmentationProceedings of Brithish Machine Vision Conference (BMVC)0 aReversible Jump Markov Chain Monte Carlo for Unsupervised MRF Co bBMVAc2004.09 a37 - 461 aKato, Zoltan1 aHoppe, Andreas1 aBarman, Sarah1 aEllis, Tim uhttp://www.bmva.org/bmvc/2004/papers/paper_223.pdf00504nas a2200109 4500008004100000245009100041210006900132260003100201300001400232100001700246856013100263 2004 eng d00aReversible Jump Markov Chain Monte Carlo for Unsupervised MRF Color Image Segmentation0 aReversible Jump Markov Chain Monte Carlo for Unsupervised MRF Co aMiskolctapolcac2004.01.28 a144 - 1511 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/reversible-jump-markov-chain-monte-carlo-for-unsupervised-mrf-color-image-segmentation02509nas a2200265 4500008004100000245007700041210007700118260007500195300001400270520158400284100001601868700001801884700002801902700001701930700001801947700002301965700001801988700001702006700002802023700001902051700002002070700002202090700002202112856010902134 2004 eng d00aSzámítógépes képfeldolgozás oktatása a Szegedi Tudományegyetemen0 aSzámítógépes képfeldolgozás oktatása a Szegedi Tudományegyetemen aMiskolcbNeumann János Számítógép-tudományi TársaságcJan 2004 a191 - 1963 aAz SZTE Informatikai Tanszékcsoportja által gondozott szakoktanterveiben 1993 óta szerepel a képfeldolgozás és alkalmazásainak oktatása. A kreditrendszer bevezetésével a Képfeldolgozás I. tárgy kötelező az ötéves képzésben részt vevő informatikus hallgatóknak. Ezen felül a választható szakirányok között szintén szerepel a Képfeldolgozás szakirány. A szakirányon belül különböző képpfeldolgozási területeket tárgyaló kurzusok épülnek egymásra. Az elméleti megalapozás mellett a képfeldolgozás alkalmazásaira is nagy hangsúlyt fektetünk. A kutatások illetve az orvosi alkalmazások fejlesztése során szerzett eredményeket a kötelező jellegű tárgyak mellett speciálkollégiumok keretében építjül be az otkatási anyagba. Számos hallgatónk választ a képfeldolgzás területéről témát a diplomamunkájához, dolgozataikkal rendszeresen és sikerrel szerepelnek az OTDK-n. Hallgatóink évente több hónapot tölthetnek külföldi partneregyetemeinken, ahol a kutató- és fejlesztőmunka mellett nálunk is elfogadott kurzusokat teljesíthetnek. A képfeldolgozás témakörön belül "ipari" projekt munkákban is egyre több hallgató vesz részt. A doktori programon belül is meghirdetünk képfeldolgozáshoz kapcsolódó kutatási irányokat. Az évente megrendezésre kerülő, 11-éves múltra visszatekintő Képfeldolgozó Nyári Iskolának (SSIP) eddig hatszor adott otthont Szeged. A rendszvénysorozat kiemelkedő fontosságú nemzetközi fórum hallgatóink és oktatóink számára is.
1 aNagy, Antal1 aBalogh, Emese1 aDudásné Nagy, Mariann1 aKuba, Attila1 aMáté, Eörs1 aPalágyi, Kálmán1 aKatona, Endre1 aKato, Zoltan1 aNyúl, László, Gábor1 aTanacs, Attila1 aGácsi, Zoltán1 aBarkóczy, Péter1 aSárközi, Gábor uhttps://www.inf.u-szeged.hu/publication/szamitogepes-kepfeldolgozas-oktatasa-a-szegedi-tudomanyegyetemen00615nas a2200169 4500008004100000245006700041210006500108260004600173300001400219100001600233700001700249700001900266700001500285700002000300700001800320856010700338 2003 eng d00aNon-Photorealistic Rendering and Content-Based Image Retrieval0 aNonPhotorealistic Rendering and ContentBased Image Retrieval aNew YorkbIEEE Computer Soc. Pr.c2003/// a153 - 1621 aJi, Xiaowen1 aKato, Zoltan1 aHuang, Zhiyong1 aRokne, Jon1 aKlein, Reinhard1 aWang, Wenping uhttps://www.inf.u-szeged.hu/publication/non-photorealistic-rendering-and-content-based-image-retrieval01362nas a2200157 4500008004100000245008500041210006900126260001800195300001400213520078800227100001701015700002101032700002101053700000501074856012501079 2003 eng d00aUnsupervised segmentation of color textured images using a multi-layer MRF model0 aUnsupervised segmentation of color textured images using a multi bIEEEc2003/// a961 - 9643 aHerein, 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'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.
1 aKato, Zoltan1 aPong, Ting Chuen1 aQiang, Song, Guo1 a uhttps://www.inf.u-szeged.hu/publication/unsupervised-segmentation-of-color-textured-images-using-a-multi-layer-mrf-model00645nas a2200169 4500008004100000245007700041210006900118260005900187300001400246100001700260700001600277700002000293700001900313700002100332700000500353856011700358 2002 eng d00aContent-based image retrieval using stochastic paintbrush transformation0 aContentbased image retrieval using stochastic paintbrush transfo aAix-en-ProvencebIEEE Computer Society PresscSep 2002 a944 - 9471 aKato, Zoltan1 aJi, Xiaowen1 aSziranyi, Tamas1 aTóth, Zoltán1 aCzúni, László1 a uhttps://www.inf.u-szeged.hu/publication/content-based-image-retrieval-using-stochastic-paintbrush-transformation00599nas a2200157 4500008004100000020001400041245009200055210006900147260001200216300001400228490000700242100002100249700002200270700001700292856013200309 2002 eng d a1155-433900aMarkov random fields in image processing application to remote sensing and astrophysics0 aMarkov random fields in image processing application to remote s c2002/// a117 - 1360 v121 aZerubia, Josiane1 aJalobeanu, André1 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/markov-random-fields-in-image-processing-application-to-remote-sensing-and-astrophysics01410nas a2200181 4500008004100000245007400041210006900115260003500184300001400219520076700233100001701000700002101017700002101038700001901059700001801078700001901096856011301115 2002 eng d00aMulticue MRF image segmentation: Combining texture and color features0 aMulticue MRF image segmentation Combining texture and color feat bIEEE Computer Societyc2002/// a660 - 6633 aHerein, 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. © 2002 IEEE.
1 aKato, Zoltan1 aPong, Ting Chuen1 aQiang, Song, Guo1 aKatsuri, Ranga1 aLaurendeau, D1 aSuen, Ching, Y uhttps://www.inf.u-szeged.hu/publication/multicue-mrf-image-segmentation-combining-texture-and-color-features00977nas a2200169 4500008004100000020001400041245007900055210006900134260001200203300001400215490000700229520039100236100001700627700002100644700002300665856011900688 2001 eng d a0167-865500aColor image segmentation and parameter estimation in a markovian framework0 aColor image segmentation and parameter estimation in a markovian c2001/// a309 - 3210 v223 aAn 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. © 2001 Elsevier Science B.V. All rights reserved.
1 aKato, Zoltan1 aPong, Ting Chuen1 aLee, Chung-Mong, J uhttps://www.inf.u-szeged.hu/publication/color-image-segmentation-and-parameter-estimation-in-a-markovian-framework00594nas a2200133 4500008004100000245009300041210006900134260004900203300001400252100001700266700002100283700002300304856013300327 2001 eng d00aA Markov Random Field Image Segmentation Model Using Combined Color and Texture Features0 aMarkov Random Field Image Segmentation Model Using Combined Colo aBerlin; HeidelbergbSpringer Verlagc2001/// a547 - 5541 aKato, Zoltan1 aPong, Ting Chuen1 aSkarbek, Wladyslaw uhttps://www.inf.u-szeged.hu/publication/a-markov-random-field-image-segmentation-model-using-combined-color-and-texture-features00601nas a2200181 4500008004100000020001400041245010400055210006900159260001200228300001400240490000600254100002000260700002100280700002100301700002100322700001700343856005900360 2000 eng d a1077-201400aImage segmentation using Markov random field model in fully parallel cellular network architectures0 aImage segmentation using Markov random field model in fully para c2000/// a195 - 2110 v61 aSziranyi, Tamas1 aZerubia, Josiane1 aCzúni, László1 aGeldreich, David1 aKato, Zoltan uhttp://www.sztaki.hu/~sziranyi/Papers/Sziranyi_MRF.pdf00447nas a2200097 4500008004100000245008500041210006900126260001200195100001700207856012500224 1999 eng d00aBayesian Color Image Segmentation Using Reversible Jump Markov Chain Monte Carlo0 aBayesian Color Image Segmentation Using Reversible Jump Markov C c1999///1 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/bayesian-color-image-segmentation-using-reversible-jump-markov-chain-monte-carlo00491nas a2200097 4500008004100000245008500041210006900126260005600195100001700251856012500268 1999 eng d00aBayesian Color Image Segmentation Using Reversible Jump Markov Chain Monte Carlo0 aBayesian Color Image Segmentation Using Reversible Jump Markov C aAmsterdam, The NetherlandsbERCIM/CWIcJanuary 19991 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/bayesian-color-image-segmentation-using-reversible-jump-markov-chain-monte-carlo01804nas a2200169 4500008004100000020001400041245007000055210006900125260001200194300001400206490000700220520124100227100001701468700002101485700001801506856011001524 1999 eng d a0031-320300aUnsupervised parallel image classification using Markovian models0 aUnsupervised parallel image classification using Markovian model c1999/// a591 - 6040 v323 aThis 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. © 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
1 aKato, Zoltan1 aZerubia, Josiane1 aBerthod, Mark uhttps://www.inf.u-szeged.hu/publication/unsupervised-parallel-image-classification-using-markovian-models00627nas a2200157 4500008004100000245007700041210006900118260004900187300001400236100001700250700002100267700002600288700001700314700002100331856011700352 1998 eng d00aMotion Compensated Color Video Classification Using Markov Random Fields0 aMotion Compensated Color Video Classification Using Markov Rando aBerlin; HeidelbergbSpringer Verlagc1998/// a738 - 7451 aKato, Zoltan1 aPong, Ting Chuen1 aLee, John, Chung Mong1 aChin, Roland1 aPong, Ting Chuen uhttps://www.inf.u-szeged.hu/publication/motion-compensated-color-video-classification-using-markov-random-fields00598nas a2200157 4500008004100000245012600041210006900167260001200236300001200248100001700260700002100277700002600298700002000324700002700344856006900371 1997 eng d00aColor Image Classification and Parameter Estimation in a Markovian FrameworkProceedings of Workshop on 3D Computer Vision0 aColor Image Classification and Parameter Estimation in a Markovi c1997.05 a75 - 791 aKato, Zoltan1 aPong, Ting Chuen1 aLee, John, Chung Mong1 aTsui, Hung, Tat1 aChung, Chi, Kit Ronald uhttp://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.128.156000639nas a2200157 4500008004100000245010500041210006900146260001200215300001000227100002000237700002100257700002100278700002100299700001700320856014400337 1997 eng d00aImage segmentation using Markov random field model in fully parallel cellular network architectures.0 aImage segmentation using Markov random field model in fully para c1997/// a - 171 aSziranyi, Tamas1 aZerubia, Josiane1 aCzúni, László1 aGeldreich, David1 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/image-segmentation-using-markov-random-field-model-in-fully-parallel-cellular-network-architectures00554nas a2200145 4500008004100000245007300041210006900114260001200183100002000195700002100215700002100236700002100257700001700278856011300295 1997 eng d00aMarkov Random Field Image Segmentation using Cellular Neural Network0 aMarkov Random Field Image Segmentation using Cellular Neural Net c1997///1 aSziranyi, Tamas1 aZerubia, Josiane1 aCzúni, László1 aGeldreich, David1 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/markov-random-field-image-segmentation-using-cellular-neural-network00573nas a2200121 4500008004100000245010000041210006900141260001200210100001700222700002100239700002600260856016500286 1997 eng d00aMotion Compensated Color Image Classification and Parameter Estimation in a Markovian Framework0 aMotion Compensated Color Image Classification and Parameter Esti c1997///1 aKato, Zoltan1 aPong, Ting Chuen1 aLee, John, Chung Mong uhttp://biblioteca.universia.net/html_bura/ficha/params/title/motion-compensated-color-image-classification-and-parameter-estimation-in-markovian/id/5664082.html00741nas a2200181 4500008004100000245008100041210006900122260009600191300001200287100002000299700002100319700002100340700001700361700002100378700002000399700001900419856012100438 1997 eng d00aMRF based image segmentation with fully parallel cellular nonlinear networks0 aMRF based image segmentation with fully parallel cellular nonlin aKeszthelybPannon Agrártudományi Egyetem Georgikon Mezőgazdaságtudományi KarcOct 1997 a43 - 501 aSziranyi, Tamas1 aZerubia, Josiane1 aGeldreich, David1 aKato, Zoltan1 aCzúni, László1 aSziranyi, Tamas1 aBerke, József uhttps://www.inf.u-szeged.hu/publication/mrf-based-image-segmentation-with-fully-parallel-cellular-nonlinear-networks02227nas a2200181 4500008004100000020001400041245006100055210006100116260001200177300001400189490000700203520166500210100001801875700001701893700001301910700002101923856010101944 1996 eng d a0262-885600aBayesian image classification using Markov random fields0 aBayesian image classification using Markov random fields c1996/// a285 - 2950 v143 aIn 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).
1 aBerthod, Mark1 aKato, Zoltan1 aYu, Shan1 aZerubia, Josiane uhttps://www.inf.u-szeged.hu/publication/bayesian-image-classification-using-markov-random-fields00615nas a2200157 4500008004100000245007000041210006900111260004200180300001400222100002000236700002100256700002100277700001700298700003200315856011000347 1996 eng d00aCellular Neural Network in Markov Random Field Image Segmentation0 aCellular Neural Network in Markov Random Field Image Segmentatio aNew YorkbWiley - IEEE Pressc1996/// a139 - 1441 aSziranyi, Tamas1 aZerubia, Josiane1 aGeldreich, David1 aKato, Zoltan1 a*Society, *IEEE, Circuits & uhttps://www.inf.u-szeged.hu/publication/cellular-neural-network-in-markov-random-field-image-segmentation02007nas a2200169 4500008004100000020001400041245011000055210006900165260001200234300001200246490000700258520136600265100001701631700001801648700002101666856015001687 1996 eng d a1077-316900aA Hierarchical Markov Random Field Model and Multitemperature Annealing for Parallel Image Classification0 aHierarchical Markov Random Field Model and Multitemperature Anne c1996/// a18 - 370 v583 aIn 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). © 1996 Academic Press, Inc.
1 aKato, Zoltan1 aBerthod, Mark1 aZerubia, Josiane uhttps://www.inf.u-szeged.hu/publication/a-hierarchical-markov-random-field-model-and-multitemperature-annealing-for-parallel-image-classification01101nas a2200169 4500008004100000020001400041245005300055210005200108260001200160300001600172490000600188520058900194100001800783700001700801700002100818856009200839 1995 eng d a1057-714900aDPA: a deterministic approach to the MAP problem0 aDPA a deterministic approach to the MAP problem c1995/// a1312 - 13140 v43 aDeterministic 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.1 aBerthod, Marc1 aKato, Zoltan1 aZerubia, Josiane uhttps://www.inf.u-szeged.hu/publication/dpa-a-deterministic-approach-to-the-map-problem01196nas a2200169 4500008004100000245004500041210004500086260003000131300001600161520065100177100001700828700002100845700001800866700002500884700003200909856008500941 1995 eng d00aUnsupervised adaptive image segmentation0 aUnsupervised adaptive image segmentation aPiscatawaybIEEEc1995/// a2399 - 24023 aThis 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.1 aKato, Zoltan1 aZerubia, Josiane1 aBerthod, Marc1 aPieczynski, Wojciech1 a*Society, *IEEE, Signal Pro uhttps://www.inf.u-szeged.hu/publication/unsupervised-adaptive-image-segmentation01830nas a2200157 4500008004100000245008400041210006900125260003000194300001400224520122500238100001701463700002101480700001801501700002901519856012401548 1995 eng d00aUnsupervised parallel image classification using a hierarchical Markovian model0 aUnsupervised parallel image classification using a hierarchical aPiscatawaybIEEEc1995/// a169 - 1743 aThis 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).1 aKato, Zoltan1 aZerubia, Josiane1 aBerthod, Marc1 a*Society, IEEE, Computer uhttps://www.inf.u-szeged.hu/publication/unsupervised-parallel-image-classification-using-a-hierarchical-markovian-model00711nas a2200097 4500008004100000245022200041210006900263260000900332100001700341856025500358 1994 eng d00aMulti-scale Markovian Modelisation in Computer Vision with Applications to SPOT Image Segmentation : Modélisations markoviennes multirésolutions en vision par ordinateur. Application ŕ la segmentation d'images SPOT0 aMultiscale Markovian Modelisation in Computer Vision with Applic c19941 aKato, Zoltan uhttps://www.inf.u-szeged.hu/publication/multi-scale-markovian-modelisation-in-computer-vision-with-applications-to-spot-image-segmentation-modelisations-markoviennes-multiresolutions-en-vision-par-ordinateur-application-r-la-segmentation-dimages-spot00664nas a2200145 4500008004100000245011900041210006900160260003200229300001400261100002100275700001700296700001800313700002900331856015800360 1994 eng d00aMulti-Temperature Annealing: A New Approach for the Energy-Minimization of Hierarchical Markov Random Field Models0 aMultiTemperature Annealing A New Approach for the EnergyMinimiza aLos AlamitosbIEEEc1994/// a520 - 5221 aZerubia, Josiane1 aKato, Zoltan1 aBerthod, Mark1 a*Society, IEEE, Computer uhttps://www.inf.u-szeged.hu/publication/multi-temperature-annealing-a-new-approach-for-the-energy-minimization-of-hierarchical-markov-random-field-models00531nas a2200121 4500008004100000245009700041210007000138260001200208100001700220700002100237700001800258856013300276 1994 eng d00aSegmentation hiérarchique d'images sur CM200 (Hierarchical Image Segmentation on the CM200)0 aSegmentation hiérarchique dimages sur CM200 Hierarchical Image S c1994///1 aKato, Zoltan1 aZerubia, Josiane1 aBerthod, Mark uhttps://www.inf.u-szeged.hu/publication/segmentation-hierarchique-dimages-sur-cm200-hierarchical-image-segmentation-on-the-cm20000475nas a2200121 4500008004100000245014300041210007000184260001200254100001700266700002100283700001800304856003100322 1994 eng d00aSegmentation multirésolution d'images sur SUN version 1 du 26.05.1994 (Multiresolution Image Segmentation on SUN version 1 of 26.05.1994)0 aSegmentation multirésolution dimages sur SUN version 1 du 260519 c1994///1 aKato, Zoltan1 aZerubia, Josiane1 aBerthod, Mark uhttp://www.app.asso.fr/en/00605nas a2200157 4500008004100000245006100041210006100102260006700163300001400230100001700244700002100261700001800282700002600300700001800326856010300344 1993 eng d00aBayesian Image Classification Using Markov Random Fields0 aBayesian Image Classification Using Markov Random Fields aDordrecht; Boston; LondonbKluwer Academic Publishersc1993/// a375 - 3821 aKato, Zoltan1 aZerubia, Josiane1 aBerthod, Mark1 aMohammad-Djafari, Ali1 aDemoment, Guy uhttps://www.inf.u-szeged.hu/publication/bayesian-image-classification-using-markov-random-fields-000538nas a2200169 4500008004100000245005000041210004900091260001200140100001800152700002100170700001400191700001800205700001700223700001800240700002100258856008900279 1993 eng d00aExtraction d'information dans les images SPOT0 aExtraction dinformation dans les images SPOT c1993///1 aBerthod, Mark1 aGiraudon, Gerard1 aLiu, Shan1 aMangin, Frank1 aKato, Zoltan1 aUrago, Sabine1 aZerubia, Josiane uhttps://www.inf.u-szeged.hu/publication/extraction-dinformation-dans-les-images-spot00451nas a2200121 4500008004100000245011100041210006900152260001200221100001700233700001800250700002100268856004000289 1993 eng d00aA Hierarchical Markov Random Field Model and Multi-Temperature Annealing for Parallel Image Classification0 aHierarchical Markov Random Field Model and MultiTemperature Anne c1993///1 aKato, Zoltan1 aBerthod, Mark1 aZerubia, Josiane uhttp://hal.inria.fr/inria-00074736/00504nas a2200121 4500008004100000245007000041210006800111260003700179100001700216700001800233700002100251856011000272 1993 eng d00aA Hierarchical Markov Random Field Model for Image Classification0 aHierarchical Markov Random Field Model for Image Classification bIEEE Computer Soc. Pr.cSep 19931 aKato, Zoltan1 aBerthod, Mark1 aZerubia, Josiane uhttps://www.inf.u-szeged.hu/publication/a-hierarchical-markov-random-field-model-for-image-classification01564nas a2200169 4500008004100000245007600041210006900117260003200186300001400218520092900232100001701161700001801178700002101196700003301217700002801250856011601278 1993 eng d00aMultiscale Markov random field models for parallel image classification0 aMultiscale Markov random field models for parallel image classif aLos AlamitosbIEEEc1993/// a253 - 2573 aIn 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.1 aKato, Zoltan1 aBerthod, Marc1 aZerubia, Josiane1 a*Analysis, *IEEE, Computer S1 a*Intelligence, *Machine uhttps://www.inf.u-szeged.hu/publication/multiscale-markov-random-field-models-for-parallel-image-classification01328nas a2200169 4500008004100000245007200041210006900113260002800182300001400210520069500224100001700919700001800936700002100954700003200975700003901007856011201046 1993 eng d00aParallel image classification using multiscale Markov random fields0 aParallel image classification using multiscale Markov random fie aNew YorkbIEEEc1993/// a137 - 1403 aIn 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.1 aKato, Zoltan1 aBerthod, Marc1 aZerubia, Josiane1 a*Society, *IEEE, Signal Pro1 a*Engineers, *Institute, of Electri uhttps://www.inf.u-szeged.hu/publication/parallel-image-classification-using-multiscale-markov-random-fields00441nas a2200121 4500008004100000245008400041210006900125260001200194100001700206700002100223700001800244856005700262 1992 eng d00aImage Classification Using Markov Random Fields with Two New Relaxation Methods0 aImage Classification Using Markov Random Fields with Two New Rel c1992///1 aKato, Zoltan1 aZerubia, Josiane1 aBerthod, Mark uhttp://hal.inria.fr/docs/00/07/49/54/PDF/RR-1606.pdf00535nas a2200133 4500008004100000245007200041210006900113260003700182300001400219100001700233700002100250700001800271856011200289 1992 eng d00aSatellite Image Classification Using a Modified Metropolis Dynamics0 aSatellite Image Classification Using a Modified Metropolis Dynam bIEEE Computer Soc. Pr.cMar 1992 a573 - 5761 aKato, Zoltan1 aZerubia, Josiane1 aBerthod, Mark uhttps://www.inf.u-szeged.hu/publication/satellite-image-classification-using-a-modified-metropolis-dynamics