Skip to main content

Expert Systems and Image Processing

  • Chapter
Image Analysis and Processing

Abstract

Since 1960’s Computer Image Analysis has resolved a lot of problems, from data acquisition to image interpretation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. M. Brady, Computational Approaches to Image Understanding, ACM, Computing surveys, 14, 1, pp 3–73, 1982.

    Article  MathSciNet  Google Scholar 

  2. D. A. Huffmann, Impossible objects on nonsense sentences, Machine Intelligence, 6, pp 295–323, 1978.

    Google Scholar 

  3. M. B. Clowes, on seeing things, Artificial Intelligence, 2, 1,pp 79–116, 1971.

    Article  Google Scholar 

  4. L. G. Shapiro, Computer Vision Systems. Post Present and Future.Pictorial Data Analysis, Haralick ed. Nato Asi Series 1983.

    Google Scholar 

  5. J. P. Crettez, Modélisation des voies visuelles primaires: première étape de la perception des formes. Thèse d’Etat, Paris V I, 1984.

    Google Scholar 

  6. F. Fogelman, Contributions à une théorie du calcul sur réseaux. Thèse d’Etat, Grenoble, 1985.

    Google Scholar 

  7. M. Nagao, Strategies for Human-like Image Understanding Systems,’Proceedings of Cognitiva 85, pp 26–32, CESTA, Paris.

    Google Scholar 

  8. M. Nagao, Control Strategies in Pattern Analysis, Pattern Recognition, vol 17, n°l, pp 45–56, 1984.

    Article  Google Scholar 

  9. L. C. Shapiro, R. Haralick, Structural descriptions and inexact matching, IEEE Trans Pami, 3, 1981.

    Google Scholar 

  10. U. Montanari, On the optimal detection of curves in noisy pictures. Comm. ACM., 14, n°15, pp 335–345, 1971.

    Article  Google Scholar 

  11. H. Ney, Dynamic programming as a technique for pattern recognition, Proc of 6 ICPR, pp 1119–1125, IEEE Computer Society Press, 1982.

    Google Scholar 

  12. T. Matsuyama, Knowledge organization and control structure in Image Understanding Proc of 7 ICPR, pp 1118–1127, IEEE Computer Society Press, 1984.

    Google Scholar 

  13. D. Marr, Vision, Freeman ed. San Francisco, 1982.

    Google Scholar 

  14. A. Rosenfeld, Image Analysis: problems, progress and prospects, Pattern Recognition, vol 17, n°l, pp 3–12, 1984.

    Article  Google Scholar 

  15. L. S. Davis, A. Rosenfeld, Cooperative processes for low level vision: a survey. Artificial Intelligence, vol 17, pp 245–263, 1981.

    Article  Google Scholar 

  16. J. K. Tsotsos, Knowledge of the visual process: content, form and use. Proc of 6 ICPR, pp 654–669, IEEE Computer Society Press, 1982.

    Google Scholar 

  17. R. J. Brachman, What IS-A is and isn’t: an analysis of taxonomic links in semantic networks. IEEE Computer, vol 16, n°10, pp 30–36, 1983.

    Google Scholar 

  18. 18.H. Farreny, Les Systèmes Experts principes et exemples. Cepadues ed. Toulouse, France, 1985.

    Google Scholar 

  19. A. Bonnet, L’Intelligence Artificielle: promesses et réalités, Interéditions, Paris, 1984.

    Google Scholar 

  20. F. Hayes-Roth, D.A. Waterman, D.B. Lenat, Building Expert System. Addison Wesley, 1983.

    Google Scholar 

  21. T. Matsuyama, V. Hwang, Sigma: A framework for Image Understanding. Integration of Bottom-up and Top-down Analyses. Proc. of IJCAI, pp 908–915. 1985.

    Google Scholar 

  22. A. M. Nozif, M.D. Levine, Low level image segmentation: an expert system. IEEE Trans PAMI, vol 6, pp 555–577, 1984.

    Article  Google Scholar 

  23. J. M. Chassery, C. Garbay, An iterative segmentation method based on a contextual color and shape criterion. IEEE Trans PAMI, vol 6, ppp 794–800, 1984.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1986 Plenum Press, New York

About this chapter

Cite this chapter

Chassery, J.M. (1986). Expert Systems and Image Processing. In: Cantoni, V., Levialdi, S., Musso, G. (eds) Image Analysis and Processing. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-2239-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4613-2239-9_3

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4612-9312-5

  • Online ISBN: 978-1-4613-2239-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics