Skip to main content
SpringerLink
Log in

A Fuzzy Shape Extraction Method

  • Chapter
  • First Online:
Recent Developments and the New Direction in Soft-Computing Foundations and Applications

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 361))

Abstract

This chapter presents an easily implementable method of fuzzy shape extraction for shape recognition. The method uses Fuzzy Hypermatrix-based classifiers in order to find the potential location of the target objects based on their colors, then determines the areas where the most densely occurring positive findings in order to restrict the area of operation thus speeding the process up. In these areas the edges are detected, the edges are mapped to tree structures, which are trimmed down to simple outline sequences using heuristics from the Fuzzy Hypermatrix. Finally, fuzzy information is extracted from the outlines that can be used to classify the shape with a fuzzy inference machine.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. M. Panwar, Hand gesture recognition based on shape parameters, in 2012 International Conference on Computing, Communication and Applications (ICCCA) (Dindigul, Tamilnadu, 2012), pp. 1–6

    Google Scholar 

  2. Z. Wang, B. Fan, G. Wang, F.C. Wu, Exploring local and overall ordinal information for robust feature description. IEEE Trans. Pattern Anal. Mach. Intell. PP(99), 1–1

    Google Scholar 

  3. A. Inoges, G. Lopez-Nicolas, C. Sagues, S. Llorente, Elastic hand contour matching in NIR images with a novel shape descriptor parametrization, in EUROCON 2015—International Conference on Computer as a Tool (EUROCON) (IEEE, Salamanca, 2015), pp. 1–6

    Google Scholar 

  4. X. Chen et al., Reasoning-based vision recognition for agricultural humanoid robot toward tomato harvesting, in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Hamburg, 2015), pp. 6487–6494

    Google Scholar 

  5. J. Li, X. Mei, D. Prokhorov, Deep neural network for structural prediction and lane detection in traffic scene. IEEE Trans. Neural Netw. Learn. Syst. PP(99), 1–14

    Google Scholar 

  6. A.R. Várkonyi-Kóczy, B. Tusor, J.T. Tóth, A fuzzy hypermatrix-based skin color filtering method, in Proceedings of the 19th IEEE Inernational Conference on Intelligent Engineering Systems, INES2015 (Bratislava, Slovakia, 3–5 Sept 2015), pp. 173–178

    Google Scholar 

  7. J. Serra, Image Analysis and Mathematical Morphology: Theoretical Advances (Academic Press, USA, 1982), p. 610

    Google Scholar 

  8. J. Canny, A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

    Article  Google Scholar 

  9. F. Russo, Edge detection in noisy images using fuzzy reasoning. IEEE Trans. Instrum. Meas. 47(5), 1102–1105 (1998)

    Article  Google Scholar 

  10. G. Law, Quantitative comparison of flood fill and modified flood fill algorithms. Int. J. Comput. Theory Eng. 5(3), June 2013

    Google Scholar 

  11. A.R. Várkonyi-Kóczy, B. Tusor, Human-computer interaction for smart environment applications using fuzzy hand posture and gesture models. IEEE Trans. Instrum. Measure. 60(5), 1505–1514 (2011)

    Article  Google Scholar 

  12. M. Yang, K. Kpalma, J. Ronsin, A survey of shape feature extraction techniques, in Pattern Recognition, ed. by P.-Y. Yin (IN-TECH, 2008), pp. 43–90

    Google Scholar 

  13. S. Belongie, J. Malik, J. Puzicha, Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(24), 509–521 (2002)

    Article  Google Scholar 

  14. K. Siddiqi, B.B. Kimia, A shock grammar for recognition, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR’96 (San Francisco, CA, June 1996), pp. 507–513

    Google Scholar 

  15. H. Freeman, L.S. Davis, A corner finding algorithm for chain coded curves. IEEE Trans. Comput. C-26(3), 297–303 March 1977

    Article  Google Scholar 

Download references

Acknowledgements

This work has partially been sponsored by the Hungarian National Scientific Fund under contract OTKA 105846 and the Research & Development Operational Program for the project “Modernization and Improvement of Technical Infrastructure for Research and Development of J. Selye University in the Fields of Nanotechnology and Intelligent Space”, ITMS 26210120042, co-funded by the European Regional Development Fund.

Author information

Authors and Affiliations

  1. Integrated Intelligent Systems Japanese-Hungarian Laboratory, Department of Mathematics and Informatics, J. Selye University, Komarno, Slovakia

    A. R. Várkonyi-Kóczy

  2. Integrated Intelligent Systems Japanese-Hungarian Laboratory, Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, Budapest, Hungary

    B. Tusor

  3. Department of Mathematics and Informatics, J Selye University, Komarno, Slovakia

    B. Tusor & J. T. Tóth

Authors
  1. A. R. Várkonyi-Kóczy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. Tusor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. T. Tóth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. R. Várkonyi-Kóczy .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  2. Machine Intelligence Institute, Iona College, New Rochelle, New York, USA

    Ronald R. Yager

  3. Department of Information Technology and Programming, Azerbaijan Technical University, Baku, Azerbaijan

    Shahnaz N. Shahbazova

  4. Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Marek Z. Reformat

  5. Department of Computer Science, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Várkonyi-Kóczy, A.R., Tusor, B., Tóth, J.T. (2018). A Fuzzy Shape Extraction Method. In: Zadeh, L., Yager, R., Shahbazova, S., Reformat, M., Kreinovich, V. (eds) Recent Developments and the New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 361. Springer, Cham. https://doi.org/10.1007/978-3-319-75408-6_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-75408-6_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75407-9

  • Online ISBN: 978-3-319-75408-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation