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Notes on a linguistic description as the basis for automatic image understanding

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The main paradigm of image understanding and a concept for its practical machine realisation are presented. The crucial elements of the presented approach are the formalisation of human knowledge about the class of images that are to be automatically interpreted, a linguistic description and the realization of cognitive resonance.
Rocznik
Strony
143--150
Opis fizyczny
Bibliogr. 29 poz., rys., wykr.
Twórcy
  • Departament of Automatics AGH University of Science and Technology, al. Mickiewicza 30, 30–059 Cracow, Poland
autor
  • Departament of Automatics AGH University of Science and Technology, al. Mickiewicza 30, 30–059 Cracow, Poland
  • Institute of Computer Science Technical University of Łódź, ul. Wólczanska 215, 90–924 Łódź, Poland; Systems Research Institute Polish Academy of Sciences, ul. Newelska 6, 01–447 Warsaw, Poland
Bibliografia
  • [1] Antoniou, G. and Harmelen, van F. (2008). A Semantic Web Primer, MIT Press, Cambridge, MA.
  • [2] Bowyer, K. W., Hollingsworth, K. and Flynn, P.J. (2008). Image understanding for iris biometrics: A survey, Computer Vision and Image Understanding 110(2): 281-307.
  • [3] Christensen, H. (2003). Cognitive (Vision) Systems, ERCIM News No. 53, Special Theme: Cognitive Systems, available at http://www.ercim.org/publication/Ercim/News/enw53/christensen.html
  • [4] Drummond, T. and Caelli, T. (2000). Learning task-specific object recognition and scene understanding, Computer Vision and Image Understanding 80(3): 315-348.
  • [5] Hilton, A., Fua, P. and Ronfard, R. (2006). Modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour, Computer Vision and Image Understanding 104(2-3): 87-89.
  • [6] Homenda, W. (2006). Automatic understanding of images: Integrated syntactic and semantic analysis of music notation, Proceedings of the International Joint Conference on Neural Networks, Vancouver, Canada, pp. 3026-3033.
  • [7] Leś, Z. and Tadeusiewicz, R. (2000). Shape understanding system, polygon class processing methods, in M. H. Hamza (Ed.), Signal Processing and Communications, IASTED/ACTA Press, Anaheim, pp. 447-454.
  • [8] Przelaskowski, A., Podsiadły, Marczykowska T., Wroblewska A., Boniński, P. and Bargiel, P. (2007). Computer-aided interpretation of medical images: Mammography case study, Machine Graphics & Vision 16(3/4): 347-375
  • [9] Ogiela, L., Tadeusiewicz, R. and Ogiela, M.R. (2008). Cognitive techniques in medical information systems, Computers in Biology and Medicine 38(4): 501-507.
  • [10] Ogiela, M. R. and Tadeusiewicz, R. (2001). Image understanding methods in biomedical informatics and digital imaging, Journal of Biomedical Informatics, Computers and Biomedical Research 34(6): 377-386.
  • [11] Ogiela, M. R. and Tadeusiewicz, R. (2008). Modern Computational Intelligence Methods for the Interpretation of Medical Image, Springer-Verlag, Berlin.
  • [12] Ogiela, M.R. and Tadeusiewicz, R. (2003). Artificial intelligence structural imaging techniques in visual pattern analysis and medical data understanding, Pattern Recognition 36(10): 2441-2452.
  • [13] Ogiela, M.R., Tadeusiewicz, R. and Ogiela, L. (2006). Image languages in intelligent radiological palm diagnostics, Pattern Recognition 39(11): 2157-2165.
  • [14] Ogiela, M.R., Tadeusiewicz, R. and Trzupek, M. (2008). Graphbased semantic description and information extraction in analysis of 3D coronary vessels visualizations, in C. Badica, M. Paprzycki (Eds.), Advances in Intelligent and Distributed Computing, Springer-Verlag, Berlin, pp. 303-309.
  • [15] Ogiela, L., Tadeusiewicz, R. and Ogiela, M.R. (2006a). Cognitive approach to visual data interpretation in medical information and recognition systems, in N. Zheng, X. Jiang, X. Lan (Eds.), Advances in Machine Vision, Image Processing, and Pattern Analysis, Springer-Verlag, Berlin, pp. 244-250.
  • [16] Ogiela, L., Tadeusiewicz, R. and Ogiela, M.R. (2006b). Cognitive computing in intelligent medical pattern recognition systems, in D.-S. Huang, K. Li, G.W. Irwin (Eds.), Intelligent Control and Automation, Springer-Verlag, Berlin, pp. 851-856.
  • [17] Ogiela, M.R., Tadeusiewicz, R. and Ogiela, L. (2006c). Graph image language techniques supporting radiological, hand image interpretations, Computer Vision and Image Understanding 103(2): 112-120.
  • [18] Ogiela, M.R., Tadeusiewicz, R. and Ogiela, L. (2006d). Image languages in intelligent radiological palm diagnostics, Pattern Recognition 39(11): 2157-2165.
  • [19] Sieckenius, de Suza C. (2005). The Semiotic Engineering of Human-Computer Interactions, MIT Press, Cambridge, MA.
  • [20] Tadeusiewicz, R. and Ogiela, M. R. (2002). Automatic understanding of medical images-New achievements in syntactic analysis of selected medical images, Biocybernetics and Biomedical Engineering 22(4): 17-29.
  • [21] Tadeusiewicz, R. and Ogiela, M.R. (2004). Medical Image Understanding Technology, Springer-Verlag, Berlin.
  • [22] Tadeusiewicz, R. and Szczepaniak, P.S. (2008). Basic concepts of knowledge-based image understanding, in N.T. Nguyen, E. Puchala, M. Wozniak and A. Zolnierek (Eds.), KESAMSTA 2008, Proceedings LNAI 4953, Springer-Verlag, Berlin, pp. 42-52.
  • [23] Tadeusiewicz R., Ogiela, M. R. (2005). Picture languages in automatic radiological palm interpretation, International Journal of Applied Mathematics and Computer Science 15(2): 305-312
  • [24] Tadeusiewicz, R., Ogiela, L. and Ogiela, M.R. (2008). The automatic understanding approach to systems analysis and design, International Journal of Information Management 28(1): 38-48.
  • [25] Tomczyk, A. (2005). Active hypercontours and contextual classification, Proceedings of the 5-th International Conference on Intelligent Systems Design and Applications ISDA'2005, Wrocław, Poland, pp. 256-261.
  • [26] Tomczyk, A. and Szczepaniak, P.S. (2005). On the relationship between active contours and contextual classification, in M. Kurzyński, G. S. Jo, R. J. Howlett and L. C. Jain (Eds.), Computer Recognition Systems. Proceedings of the 5-th International Conference on Computer Recognition Systems-CORES'05, Springer, Berlin, pp. 303-310.
  • [27] Tomczyk, A. and Szczepaniak, P.S. (2006). Adaptive potential active hypercontours, Proceedings of the 8-th International Conference on Artificial Intelligence and Soft Computing-ICAISC'2006, Springer-Verlag, Berlin, pp. 692-701.
  • [28] Tomczyk, A. and Szczepaniak, P.S. (2007). Contribution of active contour approach to image understanding, Proceedings of the IEEE International Workshop on Imaging Systems and Techniques IST'2007, Cracow, Poland, (on CDROM).
  • [29] Zheng, J.Y. and Tsuji, S. (1998). Generating dynamic projection images for scene representation and understanding, Computer Vision and Image Understanding 72(3): 237-256.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ1-0054-0013
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