Skip to main content
SpringerLink
Log in

Fuzzy Edge Detection on Hyperspectral Images Using Upper and Lower Operators

  • Conference paper
  • First Online:
Advances in Fuzzy Logic and Technology 2017 (EUSFLAT 2017, IWIFSGN 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 642))

  • 973 Accesses

Abstract

There exists a continuous research effort aiming to port methods for grayscale image processing to more complex imagery data. Color images were an initial target for such effort, but new technologies have lead to many other types of images. In this work we focus on hyperspectral images. Specifically, we analyze how to adapt the Upper-Lower Edge Detector (ULED) to hyperspectral images; our proposal consist of fusioning band-wise information using OWA operators.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Notes

  1. 1.

    The output of a fuzzy edge detection method is computationally equivalent to a local contrast (or total variation) map [19]. We stick to our naming, although we fully understand the strong similarities between different alternatives.

References

  1. De Baets, B.: Generalized idempotence in fuzzy mathematical morphology. In: Fuzzy Techniques in Image Processing, pp. 58–75. Physica-Verlag (2000)

    Google Scholar 

  2. Barrenechea, E., Bustince, H., De Baets, B., Lopez-Molina, C.: Construction of interval-valued fuzzy relations with application to the generation of fuzzy edge images. IEEE Trans. Fuzzy Syst. 19(5), 819–830 (2011)

    Article  Google Scholar 

  3. Beliakov, G., Pradera, A., Calvo, T.: Aggregation functions: a guide for practitioners. In: Studies in Fuzziness and Soft Computing, vol. 221. Springer (2007)

    Google Scholar 

  4. Ben Hamza, A., He, Y., Krim, H., Willsky, A.: A multiscale approach to pixel-level image fusion. Integr. Comput. Aided Eng. 12, 135–146 (2005)

    Google Scholar 

  5. Bloch, I.: Fuzzy relative position between objects in image processing: a morphological approach. IEEE Trans. Patt. Anal. Mach. Intell. 21(7), 657–664 (1999)

    Article  Google Scholar 

  6. Bustince, H., Barrenechea, E., Pagola, M., Fernandez, J.: Interval-valued fuzzy sets constructed from matrices: application to edge detection. Fuzzy Sets Syst. 160(13), 1819–1840 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  7. Caselles, V., Catté, F., Coll, T., Dibos, F.: A geometric model for active contours in image processing. Numer. Math. 66, 1–31 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  8. ElMasry, G., Kamruzzaman, M., Sun, D.W., Allen, P.: Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review. Crit. Rev. Food Sci. Nutr. 52(11), 999–1023 (2012)

    Article  Google Scholar 

  9. ElMasry, G., Sun, D.W.: Principles of hyperspectral imaging technology. In: Hyperspectral Imaging for Food Quality Analysis and Control, pp. 3–43 (2010)

    Google Scholar 

  10. Evans, A., Liu, X.U.: A morphological gradient approach to color edge detection. IEEE Trans. Image Process. 15(6), 1454–1463 (2006)

    Article  Google Scholar 

  11. Godo, L., Sandri, S.: A note on the duality between continuous t-norm and t-conorm operators. In: Proceedings of the IEEE International Conference on Fuzzy Systems, vol. 1, pp. 49–54 (2003)

    Google Scholar 

  12. Goetz, A., Vane, G., Solomon, J., Rock, B.: Imaging spectrometry for earth remote sensing. Science 228(4704), 1147–1152 (1985)

    Article  Google Scholar 

  13. González-Hidalgo, M., Massanet, S.: A fuzzy mathematical morphology based on discrete t-norms: fundamentals and applications to image processing. Soft Comput. 1–15 (2013)

    Google Scholar 

  14. Gowen, A., O’Donnell, C., Cullen, P., Downey, G., Frias, J.: Hyperspectral imaging-An emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 18(12), 590–598 (2007)

    Article  Google Scholar 

  15. Haralick, R.M., Sternberg, S.R., Zhuang, X.: Image analysis using mathematical morphology. IEEE Trans. Patt. Anal. Mach. Intell. 9(4), 532–550 (1987)

    Article  Google Scholar 

  16. Jacobson, N.P., Gupta, M.R.: Design goals and solutions for display of hyperspectral images. IEEE Trans. Geosci. Remote Sens. 43(11), 2684–2692 (2005)

    Article  Google Scholar 

  17. Karakos, D., Trahanias, P.: Generalized multichannel image-filtering structures. IEEE Trans. Image Process. 6(7), 1038–1045 (1997)

    Article  Google Scholar 

  18. Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Kluwer Academic Publishers, Dordrecht (2000)

    Book  MATH  Google Scholar 

  19. Lopez-Molina, C., Ayala-Martini, D., Lopez-Maestresalas, A., Bustince, H.: Baddeley’s delta metric for local contrast computation in hyperspectral imagery. In: Progress in Artificial Intelligence, pp. 1–12 (2017)

    Google Scholar 

  20. Lopez-Molina, C., Bustince, H., Galar, M., Fernández, J., De Baets, B.: On the use of t-conorms in the gravity-based approach to edge detection. In: Proceedings of the International Conference on Intelligent Systems Design and Applications, pp. 1347–1352 (2009)

    Google Scholar 

  21. Lopez-Molina, C., De Baets, B., Barrenechea, E., Bustince, H.: Edge detection on interval-valued images. In: Advances in Intelligent and Soft Computing, vol. 107, pp. 325–337. Springer, Berlin (2011)

    Google Scholar 

  22. Lopez-Molina, C., Marco-Detchart, C., De Miguel, L., Bustince, H., Fernandez, J., De Baets, B.: A bilateral schema for interval-valued image differentiation. In: Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 516–523 (2016)

    Google Scholar 

  23. Luo, X., Wu, X., Zhang, Z.: Regional and entropy component analysis based remote sensing images fusion. J. Intell. Fuzzy Syst. (2013). In press

    Google Scholar 

  24. Marr, D., Hildreth, E.: Theory of edge detection. Proc. Royal Soc. Lond. 207(1167), 187–217 (1980)

    Article  Google Scholar 

  25. Montagna, R., Finlayson, G.: Reducing integrability error of color tensor gradients for image fusion. IEEE Trans. Image Process. 22(10), 4072–4085 (2013)

    Article  MathSciNet  Google Scholar 

  26. Russo, F., Lazzari, A.: Color edge detection in presence of Gaussian noise using nonlinear prefiltering. IEEE Trans. Image Process. 54(1), 352–358 (2005)

    Google Scholar 

  27. Ruzon, M., Tomasi, C.: Color edge detection with the compass operator. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 160–166 (1999)

    Google Scholar 

  28. van de Sande, K., Gevers, T., Snoek, C.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Patt. Anal. Mach. Intell. 32(9), 1582–1596 (2010)

    Article  Google Scholar 

  29. Shafer, S.A.: Using color to separate reflection components. Technical report, University of Rochester, NY, USA (1984)

    Google Scholar 

  30. Socolinsky, D., Wolff, L.: Multispectral image visualization through first-order fusion. IEEE Trans. Image Process. 11(8), 923–931 (2002)

    Article  Google Scholar 

  31. Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using watershed transformation. Patt. Recogn. 43(7), 2367–2379 (2010)

    Article  MATH  Google Scholar 

  32. Yager, R.: On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans. Syst. Man Cybern. 18(1), 183–190 (1988)

    Article  MATH  Google Scholar 

  33. Yao, H., Lewis, D., Sun, P.: Spectral preprocessing and calibration techniques. In: Hyperspectral Imaging for Food Quality Analysis and Control, pp. 45–78 (2010)

    Google Scholar 

  34. Zhu, S.Y., Plataniotis, K.N., Venetsanopoulos, A.N.: Comprehensive analysis of edge detection in color image processing. Opt. Eng. 38(4), 612–625 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dpto. Proyectos e Ingenieria Rural, Universidad Publica de Navarra, Pamplona, Spain

    A. Lopez-Maestresalas, C. Perez-Roncal, S. Arazuri & C. Jarén

  2. Dpto. Automatica y Computacion, Universidad Publica de Navarra, Pamplona, Spain

    C. Lopez-Molina & H. Bustince

Authors
  1. A. Lopez-Maestresalas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Lopez-Molina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. Perez-Roncal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Arazuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. H. Bustince
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. C. Jarén
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Lopez-Maestresalas .

Editor information

Editors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Eulalia Szmidt

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Slawomir Zadrożny

  4. Department of Telecommunications and Information Processing, Bulgarian Academy of Sciences, Sofia, Bulgaria

    K. T. Atanassov

  5. WIT - Warsaw School of Information Technology, Warsaw, Poland

    Maciej Krawczak

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Lopez-Maestresalas, A., Lopez-Molina, C., Perez-Roncal, C., Arazuri, S., Bustince, H., Jarén, C. (2018). Fuzzy Edge Detection on Hyperspectral Images Using Upper and Lower Operators. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 642. Springer, Cham. https://doi.org/10.1007/978-3-319-66824-6_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-66824-6_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66823-9

  • Online ISBN: 978-3-319-66824-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation