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Markov random fields in image segmentation. Hanover, NH: Now Publishers; 2012.
Detection of Object Motion Regions in Aerial Image Pairs with a Multilayer Markovian Model. IEEE TRANSACTIONS ON IMAGE PROCESSING. 2009;18(10):2303-2315.
A higher-order active contour model of a 'gas of circles' and its application to tree crown extraction. PATTERN RECOGNITION. 2009;42(5):699-709.
Kör alakú objektumok szegmentálása magasabb rendű aktív kontúr modellek segítségével. In: Fazekas A, Hajdú A, editors. A Képfeldolgozók és Alakfelismerők Társaságának konferenciája - KÉPAF 2007. Debrecen: Képfeldolgozók és Alakfelismerők Társasága; 2007. 1. p. 133-140p.
A higher-order active contour model for tree detection. In: Tang YY, editor. Proceedings of the18th International Conference on Pattern Recognition, ICPR 2006. IEEE; 2006. 1. p. 130-133p.
An Improved `Gas of Circles' Higher-Order Active Contour Model and its Application to Tree Crown Extraction. In: Kalra P, Peleg S, editors. Proceedings of Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP). Berlin; Heidelberg; New York: Springer Verlag; 2006. 1. p. 152-161p.
A multi-layer MRF model for object-motion detection in unregistered airborne image-pairs. In: , editor. Proceedings - 14th International Conference on Image Processing, ICIP 2007. Piscataway: IEEE; 2006. V. VI-p. 141-p. VI-144.
Shape Moments for Region Based Active Contours. In: Chetverikov D, Czúni L, Vincze M, editors. Joint Hungarian-Austrian conference on image processing and pattern recognition. 5th conference of the Hungarian Association for Image Processing and Pattern Recognition (KÉPAF), 29th workshop of the Austrian Association for Pattern Reco. Vienna: OCG; 2005. 1. p. 187-194p.
Markov random fields in image processing application to remote sensing and astrophysics. JOURNAL DE PHYSIQUE IV. 2002;12(1):117-136.
Image segmentation using Markov random field model in fully parallel cellular network architectures. REAL-TIME IMAGING. 2000;6(3):195-211.
Unsupervised parallel image classification using Markovian models. PATTERN RECOGNITION. 1999;32(4):591-604.
Image segmentation using Markov random field model in fully parallel cellular network architectures..; 1997.
MRF based image segmentation with fully parallel cellular nonlinear networks. In: Sziranyi T, Berke J, editors. A Képfeldolgozók és Alakfelismerők Társaságának konferenciája - KÉPAF 1997. Keszthely: Pannon Agrártudományi Egyetem Georgikon Mezőgazdaságtudományi Kar; 1997. 4. p. 43-50p.
Bayesian image classification using Markov random fields. IMAGE AND VISION COMPUTING. 1996;14(4):285-295.
Cellular Neural Network in Markov Random Field Image Segmentation. In: 1996 FOURTH IEEE INTERNATIONAL WORKSHOP ON CELLULAR NEURAL NETWORKS AND THEIR APPLICATIONS, PROCEEDINGS (CNNA-96). New York: Wiley - IEEE Press; 1996. 1. p. 139-144p.
A Hierarchical Markov Random Field Model and Multitemperature Annealing for Parallel Image Classification. GRAPHICAL MODELS AND IMAGE PROCESSING. 1996;58(1):18-37.
DPA: a deterministic approach to the MAP problem. IEEE TRANSACTIONS ON IMAGE PROCESSING. 1995;4(9):1312-1314.
Unsupervised adaptive image segmentation. In: *Society *IEEESignal Pro, editor. ICASSP-95. Piscataway: IEEE; 1995. 2. p. 2399-2402p.
Unsupervised parallel image classification using a hierarchical Markovian model. In: *Society IEEEComputer, editor. Proceedings of the 5th International Conference on Computer Vision. Piscataway: IEEE; 1995. 1. p. 169-174p.
Multi-Temperature Annealing: A New Approach for the Energy-Minimization of Hierarchical Markov Random Field Models. In: *Society IEEEComputer, editor. Proceedings of the 12th IAPR International Conference on Pattern Recognition. Los Alamitos: IEEE; 1994. 5. p. 520-522p.