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Selected Publications of the Department of Image Processing and Computer Graphics of the year 1998

Books and proceedings

1. Gabor T. Herman and Attila Kuba, editors. Discrete Tomography (Special Issue), volume 9. John Wiley & Sons, Inc., 1998. [doi:10.1002/(SICI)1098-1098(1998)9:2/3<67::AID-IMA1>3.0.CO;2-L]
   Abstract: no abstract
   
   @BOOK{Herman1998,
   PUBLISHER = {John Wiley & Sons, Inc.},
   TITLE = {Discrete Tomography (Special Issue)},
   YEAR = {1998},
   EDITOR = {Gabor T. Herman and Attila Kuba},
   NUMBER = {2-3},
   VOLUME = {9},
   PAGES = {67-188},
   DOI = {10.1002/(SICI)1098-1098(1998)9:2/3<67::AID-IMA1>3.0.CO;2-L},
   }

Articles in journal or book chapters

1. J. H. B. Kemperman and Attila Kuba. Reconstruction of two-valued matrices from their two projections. International Journal of Imaging Systems and Technology, 9(2-3):110-117, December 1998. [doi:10.1002/(SICI)1098-1098(1998)9:2/3<110::AID-IMA7>3.0.CO;2-E]
   Abstract: A matrix is said to be two-valued if its elements assume at most two different values. We studied the problem of reconstructing a two-valued matrix from its marginals - that is, from its row sums and column sums - but without any knowledge of the value pair on hand. Provided at least one of these marginals is nonconstant, only finitely many (though possibly many) value pairs can lead to a valid reconstruction. Our considerations lead to an efficient algorithm for calculating all possible solutions, each with its own value pair. Special attention is given to uniqueness pairs - that is, value pairs to which there corresponds precisely one matrix having the correct marginals. Unless both marginals are constant, there can be no more than two uniqueness pairs.
   
   @ARTICLE{Kemperman1998,
   AUTHOR = {J. H. B. Kemperman and Attila Kuba},
   JOURNAL = {International Journal of Imaging Systems and Technology},
   TITLE = {Reconstruction of two-valued matrices from their two projections},
   YEAR = {1998},
   MONTH = {December},
   NUMBER = {2-3},
   PAGES = {110-117},
   VOLUME = {9},
   DOI = {10.1002/(SICI)1098-1098(1998)9:2/3<110::AID-IMA7>3.0.CO;2-E},
   }


2. Juha Kivijarvi, Tiina Ojala, Timo Kaukoranta, Attila Kuba, Laszlo G. Nyul, and Olli Nevalainen. A comparison of lossless compression methods for medical images. Computerized Medical Imaging and Graphics, 22:323-339, 1998. [PDF] [doi:10.1016/S0895-6111(98)00042-1]
   Abstract: In this work, lossless grayscale image compression methods are compared on a medical image database. The database contains 10 different types of images with bit rates varying from 8 to 16 bits per pixel. The total number of test images was about 3000, originating from 125 different patient studies. Methods used for compressing the images include seven methods designed for grayscale images and 18 ordinary general-purpose compression programs. Furthermore, four compressed image file formats were used. The results show that the compression ratios strongly depend on the type of the image. The best methods turned out to be TMW, CALIC and JPEG-LS. The analysis step in TMW is very time-consuming. CALIC gives high compression ratios in a reasonable time, whereas JPEG-LS is nearly as effective and very fast.
   
   @ARTICLE{Kivijarvi1998,
   AUTHOR = {Juha Kivijarvi and Tiina Ojala and Timo Kaukoranta and Attila Kuba and Laszlo G. Nyul and Olli Nevalainen},
   JOURNAL = {Computerized Medical Imaging and Graphics},
   TITLE = {A comparison of lossless compression methods for medical images},
   YEAR = {1998},
   PAGES = {323-339},
   VOLUME = {22},
   DOI = {10.1016/S0895-6111(98)00042-1},
   }


3. Kalman Palagyi and Attila Kuba. A 3D 6-subiteration thinning algorithm for extracting medial lines. Pattern Recognition Letters, 19:613-627, 1998. [PDF]
   Abstract: Thinning is a frequently used method for extracting skeletons in discrete spaces. This paper presents an efficient parallel thinning algorithm that directly extracts medial lines from elongated 3D binary objects (i.e., without creating medial surface). Our algorithm provides good results, preserves topology and it is easy to implement.
   
   @ARTICLE{PalagyiKubaPRL1998,
   AUTHOR = {Kalman Palagyi and Attila Kuba},
   JOURNAL = {Pattern Recognition Letters},
   TITLE = {A 3D 6-subiteration thinning algorithm for extracting medial lines},
   YEAR = {1998},
   PAGES = {613-627},
   VOLUME = {19},
   }


4. Kalman Palagyi and Attila Kuba. A hybrid thinning algorithm for 3D medical images. Journal of Computing and Information Technology, 6:149-164, 1998. [PDF]
   Abstract: Thinning is a frequently used method for extracting skeletons in discrete spaces. This paper presents an efficient parallel algorithm for thinning elongated 3D binary objects (e.g., bony structures, vessel trees, or airway trees). The proposed algorithm directly extracts medial lines as shape features from 3D binary objects by applying a brand-new class of thinning strategy called hybrid method. Our topology preserving algorithm makes easy implementation possible and gives satisfactory results for synthetic data tests and for MR angiography brain studies.
   
   @ARTICLE{PalagyiKubaCIT1998,
   AUTHOR = {Kalman Palagyi and Attila Kuba},
   JOURNAL = {Journal of Computing and Information Technology},
   TITLE = {A hybrid thinning algorithm for 3D medical images},
   YEAR = {1998},
   PAGES = {149-164},
   VOLUME = {6},
   }

Conference articles

1. Zoltan Kato, Ting Chuen Pong, and John Chung Mong Lee. Motion Compensated Color Video Classification Using Markov Random Fields. In Roland Chin and Ting Chuen Pong, editors, Proceedings of the Asian Conference on Computer Vision, volume 1351 of Lecture Notes in Computer Science, Hong Kong, China, pages 738-745, January 1998. Springer Verlag. [PDF] [PS]
   Abstract: This paper deals with the classification of color video sequences using Markov Random Fields (MRF) taking into account motion information. The theoretical framework relies on Bayesian estimation associated with MRF modelization and combinatorial optimization (Simulated Annealing). In the MRF model, we use the CIE-luv color metric because it is close to human perception when computing color differences. In addition, intensity and chroma information is separated in this space. The sequence is regarded as a stack of frames and both intra- and inter-frame cliques are defined in the label field. Without motion compensation, an inter-frame clique would contain the corresponding pixel in the previous and next frame. In the motion compensated model, we add a displacement field and it is taken into account in inter-frame interactions. The displacement field is also a MRF but there are no inter-frame cliques. The Maximum A Posteriori (MAP) estimate of the label and displacement field is obtained through Simulated Annealing. Parameter estimation is also considered in the paper and results are shown on color video sequences using both the simple and motion compensated models.
   
   @INPROCEEDINGS{Kato-etal98,
   AUTHOR = {Zoltan Kato and Ting Chuen Pong and John Chung Mong Lee},
   BOOKTITLE = {Proceedings of the Asian Conference on Computer Vision},
   TITLE = {Motion Compensated Color Video Classification Using Markov Random Fields},
   YEAR = {1998},
   ADDRESS = {Hong Kong, China},
   EDITOR = {Roland Chin and Ting Chuen Pong},
   MONTH = {January},
   PAGES = {738--745},
   PUBLISHER = {Springer Verlag},
   SERIES = {Lecture Notes in Computer Science},
   VOLUME = {1351},
   PS = {../papers/accv98.ps},
   }

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