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Selected Publications of the Department of Image Processing and Computer Graphics of the year 1995

Articles in journal or book chapters

1. Mark Berthod, Zoltan Kato, and Josiane Zerubia. DPA: A Deterministic Approach to the MAP. IEEE Transactions on Image Processing, 4(9):1312-1314, September 1995.
   Abstract: Deterministic pseudo-annealing (DPA) is a new deterministic optimization method for finding the maximum a posteriori (MAP) labeling in a Markov random field, in which the probability of a tentative labeling is extended to a merit function on continuous labelings. This function is made convex by changing its definition domain. This unambiguous maximization problem is solved, and the solution is followed down to the original domain, yielding a good, if suboptimal, solution to the original labeling assignment problem. The performance of DPA is analyzed on randomly weighted graphs
   @ARTICLE{Berthod-etal95c,
   AUTHOR = {Mark Berthod and Zoltan Kato and Josiane Zerubia},
   JOURNAL = {IEEE Transactions on Image Processing},
   TITLE = {DPA: A Deterministic Approach to the MAP},
   YEAR = {1995},
   MONTH = {September},
   NUMBER = {9},
   PAGES = {1312--1314},
   VOLUME = {4},
   }
   


2. Attila Kuba. Reconstruction of Unique Binary Matrices with Prescribed Elements. Acta Cybernetica, 12(1):57-70, 1995. [WWW]
   Abstract: The reconstruction of a binary matrix from its row and column sum vectors is considered when some elements of the matrix may be prescribed and the matrix is uniquely determined from these data. It is shown that the uniqueness of such a matrix is equivalent to the impossibility of selecting certain sequences from the matrix elements. The unique matrices are characterized by several properties. Among others it is proved that their rows and columns can be permutated such that the 1's are above and left to the (non-prescribed) 0's. Furthermore, an algorithm is given to decide if the given projections and prescribed elements determine a binary matrix uniquely, and, if the answer is yes, to reconstruct it.
   @ARTICLE{Kuba375983,
   AUTHOR = {Attila Kuba},
   JOURNAL = {Acta Cybernetica},
   TITLE = {Reconstruction of Unique Binary Matrices with Prescribed Elements},
   YEAR = {1995},
   NUMBER = {1},
   PAGES = {57-70},
   VOLUME = {12},
   URL = {http://www.inf.u-szeged.hu/actacybernetica/vol12n1/kuba/kuba.xml},
   }
   

Conference articles

1. Zoltan Kato, Mark Berthod, Josiane Zerubia, and Wojciech Pieczynski. Unsupervised Adaptive Image Segmentation. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing, volume 4, Detroit, Michigan, USA, pages 2399-2402, May 1995. IEEE. [PDF]
   Abstract: This paper deals with the problem of unsupervised Bayesian segmentation of images modeled by Markov random fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing, ICM, etc...). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the available image only. Our approach consists of a recent iterative method of estimation, called iterative conditional estimation (ICE), applied to a monogrid Markovian image segmentation model. The method has been tested on synthetic and real satellite images.
   @INPROCEEDINGS{Kato-etal95a,
   AUTHOR = {Zoltan Kato and Mark Berthod and Josiane Zerubia and Wojciech Pieczynski},
   BOOKTITLE = {Proceedings of the International Conference on Acoustics, Speech and Signal Processing},
   TITLE = {Unsupervised Adaptive Image Segmentation},
   YEAR = {1995},
   ADDRESS = {Detroit, Michigan, USA},
   MONTH = {May},
   ORGANIZATION = {IEEE},
   PAGES = {2399--2402},
   VOLUME = {4},
   }
   


2. Zoltan Kato, Josiane Zerubia, and Mark Berthod. Unsupervised Parallel Image Classification Using a Hierarchical Markovian Model. In Proceedings of the International Conference on Computer Vision, Cambridge, MA, USA, pages 169-174, June 1995. IEEE. [PDF] [PS]
   Abstract: This paper deals with the problem of unsupervised classification of images modeled by Markov Random Fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing, ICM, etc\ldots ). 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).
   @INPROCEEDINGS{Kato-etal95b,
   AUTHOR = {Zoltan Kato and Josiane Zerubia and Mark Berthod},
   BOOKTITLE = {Proceedings of the International Conference on Computer Vision},
   TITLE = {Unsupervised Parallel Image Classification Using a Hierarchical Markovian Model},
   YEAR = {1995},
   ADDRESS = {Cambridge, MA, USA},
   MONTH = {June},
   ORGANIZATION = {IEEE},
   PAGES = {169--174},
   PS = {../papers/iccv95.ps},
   }
   


3. Endre Katona, Kalman Palagyi, and Nandor Toth. Signature verification using neural nets. In Proceedings of the Scandinavian Conference on Image Analysis, Uppsala, Sweden, pages 1115-1122, June 1995. [PDF]
   Abstract: It is a hard problem to decide whether two signatures - given as scanned binary images - are written by the same person or not. The present paper gives a complex strategy to solve this problem, including thinning of the scanned image, raster-to-vector conversion, extracting features from vectorgraph representation and a decision process using a back propagation neural network model. Some aspects of neural fine tuning and learning are discussed. Features and experiences of the software implementation are mentioned.
   @INPROCEEDINGS{Katona1995,
   AUTHOR = {Endre Katona and Kalman Palagyi and Nandor Toth},
   BOOKTITLE = {Proceedings of the Scandinavian Conference on Image Analysis},
   TITLE = {Signature verification using neural nets},
   YEAR = {1995},
   ADDRESS = {Uppsala, Sweden},
   MONTH = {June},
   PAGES = {1115-1122},
   }
   

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