The Descriptive Approach to Image Analysis Current State and Prospects

  • I. Gurevich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

The presentation is devoted to the research of mathematical fundamentals for image analysis and recognition procedures. The final goal of this research is automated image mining: a) automated design, test and adaptation of techniques and algorithms for image recognition, estimation and understanding; b) automated selection of techniques and algorithms for image recognition, estimation and understanding; c) automated testing of the raw data quality and suitability for solving the image recognition problem. The main instrument is the Descriptive Approach to Image Analysis, which provides: 1) standardization of image analysis and recognition problems representation; 2) standardization of a descriptive language for image analysis and recognition procedures; 3) means to apply common mathematical apparatus for operations over image analysis and recognition algorithms, and over image models. It is shown also how and where to link theoretical results in the foundations of image analysis with the techniques used to solve application problems.

References

  1. 1.
    Gurevich, I.B.: The Descriptive Framework for an Image Recognition Problem. In: Proceedings of The 6th Scandinavian Conference on Image Analysis, Oulu, June 19 - 22 (1989); In 2 volumes. - Pattern Recognition Society of Finland, vol. 1, pp. 220–227 (1989)Google Scholar
  2. 2.
    Gurevich, I.B.: Descriptive Technique for Image Description, Representation and Recognition. Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications in the USSR 1(1), 50–53 (1991)Google Scholar
  3. 3.
    Gurevich, I.B., Yashina, V.V.: Application of algebraic language in image analysis. Illustrative example. In: Proceedings of the 7th International conference Pattern Recognition and Image Analysis: New Information Technologies (PRIA-7-2004), St. Petersburg, Russian Federation, vol. 1, pp. 240–243 (2004)Google Scholar
  4. 4.
    Gurevich, I.B., Yashina, V.V.: Conditions of generating descriptive image algebras by a set of image processing operations. In: Sanfeliu, A., Ruiz-Shulcloper, J. (eds.) CIARP 2003. LNCS, vol. 2905, pp. 498–505. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Gurevich, I.B., Yashina, V.V.: Descriptive Image Algebras with One Ring. Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Application 13(4), 579–599 (2003)Google Scholar
  6. 6.
    Gurevich, I.B., Zhuravlev, Y.I.: An Image Algebra Accepting Image Models and Image Transforms. In: Proceedings of the 7th International Workshop Vision, Modeling, and Visualization 2002 (VMV 2002), Erlangen, Germany, November 20 – 22, pp. 21–26. IOS Press, Amsterdam (2002)Google Scholar
  7. 7.
    Ritter, G.X., Wilson, J.N.: Handbook of Computer Vision Algorithms in Image Algebra, 2nd edn. CRC Press Inc., Boca Raton (2001)MATHGoogle Scholar
  8. 8.
    Zhuravlev, Y.I.: An Algebraic Approach to Recognition or Classification Problems. Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications 8(1), 59–100 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • I. Gurevich
    • 1
  1. 1.Department of Mathematical and Applied Techniques for Image Analysis and Nonlinear ProblemsDorodnicyn Computing Centre of the Russian Academy of SciencesMoscowThe Russian Federation

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