On Image Representing in Image Analysis

Igor Gurevich, Vera Yashina

Abstract

The presentation is devoted to the research of mathematical fundamentals for image analysis and recognition procedures being conducted currently in the Dorodnicyn Computing Centre of the Russian Academy of Sciences, Moscow, Russian Federation. The paper presents and discusses the main results obtained using the Descriptive Approach to Analysing and Understanding of Images when solving fundamental problems of the formalization and systematization of the methods and forms of representing information in the problems of the analysis, recognition, and understanding of images. In particular, the problems arise in connection with the automation of information extraction from images in order to make intelligent decisions (diagnostics, prediction, detection, evaluation, and identification of patterns). 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.

References

  1. Grenander, U., 1993. General Pattern Theory, A Mathematical Study of Regular Structure. Clarendon Press, Oxford.
  2. Grenander, U., 1996. Elements of Pattern Theory, The Johns Hopkins University Press.
  3. Gurevich, I., 1989. The Descriptive Framework for an Image Recognition Problem. In Proceedings of the 6th Scandinavian Conference on Image Analysis, vol. 1, pp. 220 - 227. Pattern Recognition Society of Finland.
  4. Gurevich, I., 1991. Descriptive Technique for Image Description, Representation and Recognition. In Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications in the USSR, vol. 1, pp. 50 - 53. MAIK “Interpreodika”.
  5. Gurevich, I., 2005. The Descriptive Approach to Image Analysis. Current State and Prospects. In Proceedings of 14th Scandinavian Conference on Image Analysis, LNCS 3540, pp. 214-223. Springer-Verlag Berlin Heidelberg.
  6. Gurevich, I., Jernova, I., 2003. The Joint Use of Image Equivalents and Image Invariants in Image Recognition. In Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, vol. 13, no.4, pp. 570-578. MAIK "Nauka/Interperiodica".
  7. Gurevich, I., Koryabkina, I., 2006. Comparative Analysis and Classification of Features for Image Models. In Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, vol.16, no.3, pp. 265-297. MAIK "Nauka/Interperiodica"/Pleiades Publishing, Inc.
  8. Gurevich, I., Yashina, V., 2006. Operations of Descriptive Image Algebras with One Ring. In Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, vol.16, no.3, pp. 298-328. MAIK "Nauka/Interperiodica"/Pleiades Publishing, Inc.
  9. Gurevich, I., Yashina, V., 2006. Computer-Aided Image Analysis Based on the Concepts of Invariance and Equivalence. In Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, vol.16, no.4, pp.564-589. MAIK "Nauka/Interperiodica"/Pleiades Publishing, Inc.
  10. Gurevich, I., Yashina, V., 2008. Descriptive Approach to Image Analysis: Image Models. In Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications. MAIK "Nauka/Interperiodica"/Pleiades Publishing, Inc., vol.18, no.4, pp. 518-541.
  11. Gurevich, I., Yashina, V., Koryabkina, I., Niemann, H., Salvetti, O., 2008. Descriptive Approach to Medical Image Mining. An Algorithmic Scheme for Analysis of Cytological Specimens. In Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, vol.18, no.4, pp. 542-562. MAIK "Nauka/Interperiodica"/Pleiades Publishing, Inc.
  12. Gurevich, I., Yashina, V., 2012. Descriptive Approach to Image Analysis: Image Formalization Space. In Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, vol. 22, no. 4, pp. 495-518. Pleiades Publishing, Inc.
  13. Ritter, G., Wilson, J., 2001. Handbook of Computer Vision Algorithms in Image Algebra. 2-d Edition. CRC Press Inc.
  14. Ritter, G., 2001. Image Algebra. Center for computer vision and visualization, Department of Computer and Information science and Engineering, University of Florida, Gainesville, FL 32611.
  15. Sternberg, S., 1985. An overview of Image Algebra and Related Architectures. Integrated Technology for parallel Image Processing (S. Levialdi, ed.), London: Academic Press.
  16. Marr, D., 1982. Vision. Freeman, New York.
  17. Zhuravlev, Yu., 1998. An Algebraic Approach to Recognition and Classification Problems. In Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, vol.8, pp.59- 100. MAIK "Nauka/Interperiodica".
Download


Paper Citation


in Harvard Style

Gurevich I. and Yashina V. (2015). On Image Representing in Image Analysis . In Proceedings of the 5th International Workshop on Image Mining. Theory and Applications - Volume 1: IMTA-5, (VISIGRAPP 2015) ISBN 978-989-758-094-9, pages 29-37. DOI: 10.5220/0005460300290037


in Bibtex Style

@conference{imta-515,
author={Igor Gurevich and Vera Yashina},
title={On Image Representing in Image Analysis},
booktitle={Proceedings of the 5th International Workshop on Image Mining. Theory and Applications - Volume 1: IMTA-5, (VISIGRAPP 2015)},
year={2015},
pages={29-37},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005460300290037},
isbn={978-989-758-094-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Workshop on Image Mining. Theory and Applications - Volume 1: IMTA-5, (VISIGRAPP 2015)
TI - On Image Representing in Image Analysis
SN - 978-989-758-094-9
AU - Gurevich I.
AU - Yashina V.
PY - 2015
SP - 29
EP - 37
DO - 10.5220/0005460300290037