Skip to main content
SpringerLink
Log in

Method for Edges Detection in Digital Images Through the Use of Cellular Automata

  • Conference paper
  • First Online:
Advances and Applications in Computer Science, Electronics and Industrial Engineering (CSEI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1078))

Abstract

In the present article, an algorithm based on cellular automata for noise elimination and edge detection in grayscale images is proposed. However, the focus of this project will be on the process of identifying contours, since this represents a higher challenge at the research level. Also, the cellular automaton has an adaptive behavior, which allows it to expand when it considers that the information coming from its initial neighbors is insufficient to determine if the pixel in evaluation is a “border pixel” or not, as this is an important feature together with a set of transition rules useful to accentuate relevant details within the image. By integrating these characteristics, we find the results obtained by the Proposed Algorithm presented greater similarity compared to images with ideal borders, since the edge recovery ranges between 92.30% and 97.21%, which indicates that MEDCA is in general terms, more efficient compared to similar algorithms.

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bhardwaj, S., Mittal, A.: A survey on various edge detector techniques. Procedia Technol. 4, 220–226 (2012)

    Article  Google Scholar 

  2. Diwakar, M., Kumar, P., Gupta, K.: Cellular automata based edge-detection for brain tumor. In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), India, pp. 53–59. IEEE (2013)

    Google Scholar 

  3. Rosin, P., Sun, X.: Edge detection using cellular automata. In: Cellular Automata in Image Processing and Geometry, pp. 85–103. Springer, Cham (2014)

    Google Scholar 

  4. Rosin, P., Sun, X.: Cellular automata as a tool for image processing. Emerg. Top. Comput. Vis. Appl. 1, 233–251 (2011)

    Google Scholar 

  5. Rosin, P.L.: Training cellular automata for image processing. IEEE Trans. Image Process. 15(7), 2076–2087 (2006)

    Article  Google Scholar 

  6. Wongthanavasu, S., Sadananda, R.: A CA-based edge operator and its performance evaluation. J. Vis. Commun. Image Represent. 14(2), 83–96 (2003)

    Article  Google Scholar 

  7. Selvapeter, J., Hordijk, W.: Genetically evolved cellular automata for image edge detection. In: International Conference on Signal, Image Processing and Pattern Recognition, India (2013)

    Google Scholar 

  8. Batouche, M., Meshoul, S., Abbassene, A.: On solving edge detection by emergence. Adv. Appl. Artif. Intell. 4031, 800–808 (2006)

    Article  Google Scholar 

  9. Slatnia, S., Batouche, M., Melkemi, K.E.: Evolutionary cellular automata based-approach for edge detection. Appl. Fuzzy Sets Theory 4578, 404–411 (2007)

    Article  Google Scholar 

  10. Yang, C., Ye, H., Wang, G.: Cellular automata modeling in edge recognition. In: Seventh International Symposium on Artificial Life and Robotics, pp. 128–132 (2002)

    Google Scholar 

  11. Tourtounis, D., Mitianaudis, N., Sirakaulis, G.: Salt-n-pepper noise filtering using cellular automata. J. Cell. Automata 13(1–2), 81–101 (2018)

    MathSciNet  Google Scholar 

  12. Chickerur, S., Kumar, A.: Color image restoration using neural network model. J. Univ. Comput. Sci. 17(1), 107–125 (2011)

    Google Scholar 

  13. Betancourt, A., Mujica, A., Tapias, H.: Procesamiento difuso de imágenes: filtro difuso para eliminar el ruido impulsivo. Rev. Ing. 8(2), 40–46 (2003)

    Google Scholar 

  14. Dhillon, P.K.: A novel framework to image edge detection using cellular automata. In: IJCA Special Issue on Confluence 2012 - The Next Generation Information Technology Summit, Confluence, vol. 1, pp. 1–5 (2012)

    Google Scholar 

  15. Neumann, J.V.: Theory of self-reproducing automata. In: Burks, A.W. (ed.) Machine Theory, pp. 91–131. University of Illinois Press, Urbana (1966)

    Google Scholar 

  16. Shukla, A.P.: Training cellular automata for image edge detection. Rom. J. Inf. Sci. Technol. 19(4), 338–359 (2016)

    Google Scholar 

  17. Pan, P.Z., Feng, X.T., Zhou, H.: Solid cellular automaton method for the solution of physical field. In: 2009 WRI World Congress on Computer Science and Information Engineering, Los Angeles, CA, USA, pp. 765–768. IEEE (2009)

    Google Scholar 

  18. Kumar, T., Sahoo, G.: A novel method of edge detection using cellular automata. Int. J. Comput. Appl. 9(4), 38–44 (2010)

    Google Scholar 

  19. Hasanzadeh, M., Sadeghi, S., Rezvanian, A., Reza, M.: Cellular edge detection: combining cellular automata and cellular learning automata. AEU – Int. J. Electron. Commun. 69(9), 1282–1290 (2015)

    Article  Google Scholar 

  20. Wongthanavasu, S.: Cellular automata for medical image processing. In: Salcido, A. (ed.) Cellular Automata - Innovative Modelling for Science and Engineering, pp. 395–410 (2011)

    MATH  Google Scholar 

  21. Chacón, M., Sandoval, R., Vega, J.: Percepción visual aplicada a la robótica, 1st edn. Alfaomega Grupo Editor, Mexico (2015)

    Google Scholar 

  22. Costa, A.F.: Una Introducción a la Simetría. UNED - Universidad Nacional de Educación a Distancia, Bogotá (2009)

    Google Scholar 

  23. Roberts, F., Tesman, B.: Applied Combinatorics, 2nd edn. Chapman & Hall/CRC, Boca Raton (2009)

    MATH  Google Scholar 

  24. Öztürk, Ş., Akdemir, B.: Comparison of edge detection algorithms for texture analysis on glass production. Procedia Soc. Behav. Sci. 195, 2675–2682 (2015)

    Article  Google Scholar 

  25. Patil, M., Dhopeshwarkar, M., Sathe, P.: Calculate the quality measures on classification of continuous EEG without trial structure EEG dataset. Int. J. Comput. Appl. 147(10), 32–35 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Systems Engineering, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Karen Angulo & Danilo Gil

  2. Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Helbert Espitia

Authors
  1. Karen Angulo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Danilo Gil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Helbert Espitia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Karen Angulo .

Editor information

Editors and Affiliations

  1. Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland

    Jyrki Nummenmaa

  2. Faculty of Engineering in Bilbao, University of the Basque Country (UPV/EHU), Bilbao, Spain

    Federico Pérez-González

  3. Department of Management, Escola Tècnica Superior d’Enginyeria Industrial de Barcelona (ETSEIB), Universitat Politècnica de Catalunya, Barcelona, Spain

    Bruno Domenech-Lega

  4. Division of Geotechnical Engineering and Geosciences; Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain

    Jean Vaunat

  5. Faculty of Software, Electronics and Industrial Engineering, Universidad Técnica de Ambato, Ambato, Ecuador

    Félix Oscar Fernández-Peña

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Angulo, K., Gil, D., Espitia, H. (2020). Method for Edges Detection in Digital Images Through the Use of Cellular Automata. In: Nummenmaa, J., Pérez-González, F., Domenech-Lega, B., Vaunat, J., Oscar Fernández-Peña, F. (eds) Advances and Applications in Computer Science, Electronics and Industrial Engineering. CSEI 2019. Advances in Intelligent Systems and Computing, vol 1078. Springer, Cham. https://doi.org/10.1007/978-3-030-33614-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33614-1_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33613-4

  • Online ISBN: 978-3-030-33614-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation