Skip to main content
SpringerLink
Log in

Fuzzy Lattice-Based Orthogonal Image Transformation Technique for Natural Image Analysis

  • Conference paper
  • First Online:
Artificial Intelligence and Evolutionary Computations in Engineering Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1056))

Abstract

Fuzzy lattice theory have been widely used in image processing as it allows map functions residing in an original space to functions in a transformed space, resulting in powerful knowledge extraction and image pattern recognition. Despite recognition efficiency and best means of knowledge extraction, the computational complexity and the noise rate involved have been an open problem to be addressed. In this paper, to reduce the computational complexity by optimizing the number of granules between pixels and improving the PSNR through linear fuzzy transform, a method called Euclidean Fuzzy Lattice Orthogonal Image Transform (EFL-OIT) has been presented.

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

Similar content being viewed by others

References

  1. Sussner, P.: Lattice fuzzy transforms from the perspective of mathematical morphology. Fuzzy Sets Syst. 288, 115–128 (2016)

    Article  MathSciNet  Google Scholar 

  2. Kaburlasos, V.G., Papakostas, G.A.: Learning Distributions of image features by interactive fuzzy lattice reasoning in pattern recognition applications. IEEE Comput. Intell. Mag. 10(3), 42–51 (2015)

    Article  Google Scholar 

  3. Di Martino, Ferdinando, Hurtik, Petr, Perfilieva, Irina, Sessa, Salvatore: A color image reduction based on fuzzy transforms. Inf. Sci. 266, 101–111 (2014)

    Article  Google Scholar 

  4. Perfiljeva, I., Vlasanek, P.: Image reconstruction by means of F-transform. Knowl.-Based Syst. 70, 55–63 (2014)

    Article  Google Scholar 

  5. Perez-Ornelas, F., Mendoza, O., Melin, P., Castro, J.R., Rodriguez-Diaz, A., Castillo, O.: Fuzzy index to evaluate edge detection in digital image. Plos One 1–19 (2015)

    Google Scholar 

  6. Haq, I., Anwar, S., Shah, K., Khan, M.T., Shah, S.A.: Fuzzy logic based edge detection in smooth and noisy clinical images. Plos One 1–17 (2015)

    Google Scholar 

  7. Maragos, Petros: Lattice image processing: a unification of morphological and fuzzy algebraic systems. J. Math. Imaging Vis. 22(2), 333–353 (2005)

    Article  MathSciNet  Google Scholar 

  8. Arampatzis, A., Zagoris, K., Chatzichristofis, S.A.: Dynamic two-stage image retrieval from large multimedia databases. Inf. Process. Manag. 49(1), 274–285 (2013)

    Article  Google Scholar 

  9. Bloch, I.: Fuzzy sets for image processing and understanding. Fuzzy Sets Syst. 281, 280–291 (2015)

    Article  MathSciNet  Google Scholar 

  10. Zeng, Y., Lan, J., Zou, J., Wu, C., Li, J.: A fast and robust method for image segmentation using fuzzy solutions of partial differential equations. Int. J. Signal Process., Image Process. Pattern Recognit. 8(10), 389–400 (2015)

    Google Scholar 

  11. Bloch, I.: Lattices of fuzzy sets and bipolar fuzzy sets, and mathematical morphology. Inf. Sci. 181(10), 2002–2015 (2011)

    Article  MathSciNet  Google Scholar 

  12. Linner, E.S., Moren, M., Smed, K.-O., Nysjo, J., Strand, R.: LatticeLibrary and BccFccRaycaster: software for processing and viewing 3D data on optimal sampling lattices. SoftwareX 1–9 (2016)

    Google Scholar 

  13. Grana, M.: Lattice computing: lattice theory based computational intelligence. Lattice Comput. 1–9 (2008)

    Google Scholar 

  14. Chiranjeevi, P., Sengupta, S.: Neighborhood supported model level fuzzy aggregation for moving object segmentation. IEEE Trans. Image Process. 23(2), 645–657 (2014)

    Article  MathSciNet  Google Scholar 

  15. Lindblad, J., Sladoj, N.: Linear time distances between fuzzy sets with applications to pattern matching and classification. IEEE Trans. Image Process. 23(1), 126–136 (2014)

    Article  MathSciNet  Google Scholar 

  16. Mélange, T., Nachtegael, M., Kerre, E.E.: Fuzzy random impulse noise removal from color image sequences. IEEE Trans. Image Process. 20(4), 959–970 (2011)

    Article  MathSciNet  Google Scholar 

  17. Strauss, O.: Non-additive interval-valued F-transform. Fuzzy Sets Syst. 270, 1–24 (2015)

    Article  MathSciNet  Google Scholar 

  18. Borgwardt, S., Penaloza, R.: Consistency reasoning in lattice-based fuzzy description logics. Int. J. Approx. Reason. 55(9), 1917–1938 (2014)

    Article  MathSciNet  Google Scholar 

  19. Singh, P.K., Aswani Kumar, C.: Bipolar fuzzy graph representation of concept lattice. Inf. Sci. 288, 437–448 (2014)

    Article  MathSciNet  Google Scholar 

  20. Karur, S.P.: Contributions of mathematical model in bio medical sciences-an overview. Int. J. Appl. Sci.-Res. Rev. 33–39 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Vellore Institute of Technology, Vellore, India

    S. Jagatheswari

  2. Kongu Engineering College, Erode, India

    R. Viswanathan

Authors
  1. S. Jagatheswari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Viswanathan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Jagatheswari .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Government College of Engineering, Keonjhar, India

    Subhransu Sekhar Dash

  2. Department of Software Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India

    C. Lakshmi

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  4. Department of Electrical Engineering, IIT Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jagatheswari, S., Viswanathan, R. (2020). Fuzzy Lattice-Based Orthogonal Image Transformation Technique for Natural Image Analysis. In: Dash, S., Lakshmi, C., Das, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-15-0199-9_26

Download citation

Publish with us

Policies and ethics

Search

Navigation