Skip to main content
SpringerLink
Log in

Active Hypercontour as Information Fusion Method

  • Conference paper
Book cover Computer Recognition Systems 2

Part of the book series: Advances in Soft Computing ((AINSC,volume 45))

  • 747 Accesses

Abstract

In this paper, it is shown that active hypercontours can be interpreted as a method for information fusion. The fusion concerns using of information from diverse sources and of different type (data and knowledge). Moreover, active hypercontours can work as group classifiers.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
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. Ho T.K., Hull J.J., Srihari S.N. (1994): Decision Combination in Multiple Classifier Systems. IEEE Trans, on Pattern Analysis and Machine Learning, vol. 41, pp. 723–744.

    Google Scholar 

  2. Bauer E., Kohavi R. (1999): An Empirical Comparison of Voting Classification Algorithm: Bagging, Boosting and Variants. Machine Learning, vol. 36, pp. 105–142.

    Article  Google Scholar 

  3. Ji C., Ma S. (1997): Combination of Weak Classifiers. IEEE Trans. on Pattern Analysis and Machine Learning, vol. 8, pp. 32–42.

    Google Scholar 

  4. Kittler J., Hatef M., Duin R.P.W., Matas J. (1998): On Combining Classifiers. IEEE Trans, on Pattern Analysis and Machine Learning, vol. 20, pp. 226–239.

    Article  Google Scholar 

  5. Kuncheva L.I., Jain L.C. (2000): Designing Classifier fusion Systems by Genetic Algorithms. IEEE Trans. on Evolutionary Computation, vol. 4, pp. 327–336.

    Article  Google Scholar 

  6. Kuncheva L.I., Jain L.C. (2002): Switching between Selection and Fusion in Combining Classifiers: An Experiment. IEEE Trans. on Systems, Man and Cybernetics, Part B: Cybernetics, vol. 32, pp. 156–150.

    Google Scholar 

  7. Kuncheva L.I., Whitaker C.J., Shipp CA., Duin R.P.W. (2003): Limits on the MajorityVote Accuracy in Classifier Fusion. Pattern Analysis and Applications, vol. 6, pp. 22–31.

    Article  MATH  MathSciNet  Google Scholar 

  8. Kuncheva L.I. (2004): Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, New Jersey.

    Book  MATH  Google Scholar 

  9. Kurzyski M., Woniak M. (2000): Rule-based Algorithms with Learning for Sequential Recognition Problem. Proc. of the Third Int. Conference on Information Fusion-Fusion’ 2000, Paris, France, TuC5: 24–29.

    Google Scholar 

  10. Kurzyski M. (2004): Consistency Conditions of Expert Rule Set in the Probabilistic Pattern Recognition. Lecture Notes in Computer Science, LNCS-3314, pp. 831–836.

    Google Scholar 

  11. Kurzyski M. (2005): Combining Rule-based and Sample-based Classifiers Probabilistic Approach. Lecture Notes in Computer Science, LNCS-3704, pp. 298–307.

    Article  Google Scholar 

  12. Lam L. (2000): Classifier Combinations: Implementations and Theoretical Issue. Lecture Notes in Computer Science, LNCS-1857, pp. 77–87.

    Article  Google Scholar 

  13. Woniak M. (2006): Metody fuzji informacji dla komputerowych systemw rozpoznawania (Methods for information fusion for computer recognition systems); in Polish. Oficyna Wydawnicza Politechniki Wrocawskiej, Wrocaw.

    Google Scholar 

  14. Grzeszczuk R., Levin D. (1997): Brownian Strings: Segmenting Images with Stochastically Deformable Models. IEEE Trans. on Pattern Analysis and Machine Intelligence, 19, no. 10, 100–1013.

    Article  Google Scholar 

  15. Kass M., Witkin W., Terzopoulos S. (1988): Snakes: Active Contour Models. Int. Journal of Computer Vision, vol. 1, no 4, 321–333.

    Article  Google Scholar 

  16. Caselles V. Kimmel R., Sapiro G. (2000): Geodesic Active Contours. Int. Journal of Computer Vision, 22(1), 61–79.

    Google Scholar 

  17. Tomczyk A. (2005): Active Hypercontours and Contextual Classification. Proceedings of the 5th International Conference on Intelligent Systems Design and Applications-ISDA’2005. Wroclaw, Poland, IEEE Computer Society Press, pp. 256–261.

    Google Scholar 

  18. Tomczyk A., Szczepaniak P.S. (2005): On the Relationship between Active Contours and Contextual Classification. In: M Kurzyski, et al. (Eds.): Computer Recognition Systems. Proceedings of the 4th Int. Conference on Computer Recognition Systems-CORES’05. Springer, Berlin, Heidelberg, New York, pp. 303–310.

    Google Scholar 

  19. Tomczyk A., Szczepaniak P.S. (2006): Adaptive Potential Active Hypercntours. Proceedings of the 8th International Conference on Artificial Intelligence and Soft Computing ICAISC’2006. Zakopane, Poland. Springer-Verlag, Berlin, Heidelberg; pp. 692–701.

    Google Scholar 

  20. Pedrycz W., Loia V. (2004): P-FCM: A proximity-based fuzzy clustering. Fuzzy Sets and Systems, vol. 128, pp. 21–41.

    Article  MathSciNet  Google Scholar 

  21. Tomczyk A. (2007): Image Segmentation using Adaptive Potential Active Contours. In: Computer Recognition Systems. hProceedings of the 5th Int. Conference on Computer Recognition Systems-CORES’07 (this volume).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science, Technical University of Lodz, Wolczanska 215, 90-924, Lodz, Poland

    Piotr S. Szczepaniak

  2. Systems Research Institute, Polish Academy of Sciences, ul.Newelska 6, 01-447, Warsaw, Poland

    Piotr S. Szczepaniak

Authors
  1. Piotr S. Szczepaniak
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Electronics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Marek Kurzynski , Edward Puchala , Michal Wozniak  & Andrzej Zolnierek , ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Szczepaniak, P.S. (2007). Active Hypercontour as Information Fusion Method. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds) Computer Recognition Systems 2. Advances in Soft Computing, vol 45. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75175-5_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-75175-5_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75174-8

  • Online ISBN: 978-3-540-75175-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation