Skip to main content
Springer Nature Link
Log in

New Aggregation Strategies in Color Edge Detection with HSV Images

  • Conference paper
  • First Online:
Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1602))

  • 855 Accesses

Abstract

Most edge detection algorithms only deal with grayscale images, while their use with color images remains an open problem. This paper explores different approaches to aggregating color information from RGB and HSV images for edge extraction purposes through the usage of the Canny algorithm. The Berkeley’s image data set is used to evaluate the performance of the different aggregation methods. Precision, Recall and F-score are computed. Better performance of aggregations with HSV channels than with RGB’s was found. This article also shows that depending on the type of image used -RGB or HSV-, some methodologies are more appropriate than others.

Supported by Government of Spain, grant PGC2018-096509-B-I00.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Multichannel approach was employed in [9].

  2. 2.

    A detailed explanation of the different phases of edge detection can be found in [11, 12].

  3. 3.

    This relationship between thresholds had been discovered in previous researches.

References

  1. de Baets, B., López-Molina, C.: The kermit image toolkit (kitt), ghent university. www.kermitimagetoolkit.net (2016)

  2. Beliakov, G., Bustince, H., Paternain, D.: Image reduction using means on discrete product lattices. IEEE Trans. Image Process. 21(3), 1070–1083 (2011)

    Article  MathSciNet  Google Scholar 

  3. Bogumil, S.: Color image edge detection and segmentation: a comparison of the vector angle and the Euclidean distance color similarity measures. Ph.D. thesis, University of Waterloo (1999)

    Google Scholar 

  4. Bouchon-Meunier, B.: Aggregation and fusion of imperfect information, vol. 12. Physica (2013)

    Google Scholar 

  5. Bustince, H., Fernández, J., Kolesárová, A., Mesiar, R.: Generation of linear orders for intervals by means of aggregation functions. Fuzzy Sets Syst. 220, 69–77 (2013)

    Article  MathSciNet  Google Scholar 

  6. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI–8(6), 679–698 (1986). https://doi.org/10.1109/TPAMI.1986.4767851

    Article  Google Scholar 

  7. Dutta, S.: A color edge detection algorithm in RGB color space, pp. 337–340 (2009)

    Google Scholar 

  8. Flores-Vidal, P.A., Gómez, D., Castro, J., Montero, J.: The different importance of each color in edge detection. In: Developments of Artificial Intelligence Technologies in Computation and Robotics - Proceedings of the 14th International FLINS Conference (FLINS2020), pp. 931–938 (2020)

    Google Scholar 

  9. Flores-Vidal, P.A., Gómez, D., Castro, J., Montero, J.: A new approach to color edge detection by means of transforming RGB images into an 8-dimension color space. In: Proceedings of the EEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2919), pp. 1140–1147 (2020)

    Google Scholar 

  10. Flores-Vidal, P.A., Gómez, D., Villarino, G., Castro, J., Montero, J.: A new approach to color edge detection. In: Atlantis Studies in Uncertainty Modelling, 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), pp. 376–384 (2019)

    Google Scholar 

  11. Flores-Vidal, P.A., Olaso, P., Gómez, D., Guada, C.: A new edge detection method based on global evaluation using fuzzy clustering. Soft. Comput. 23(6), 1809–1821 (2018). https://doi.org/10.1007/s00500-018-3540-z

    Article  Google Scholar 

  12. Flores Vidal, P.A., Villarino, G., Gómez, D., Montero, J.: A new edge detection method based on global evaluation using supervised classification algorithms. Int. J. Comput. Intell. Syst. 12(1), 367–378 (2019)

    Article  Google Scholar 

  13. Gnanatheja, R., Reddy, T.S.: YCoCg color image edge detection. Int. J. Eng. Res. Appl. 2(2), 152–156 (2012)

    Google Scholar 

  14. Goguen, J.A.: L-fuzzy sets. J. Math. Anal. Appl. 18(1), 145–174 (1967)

    Article  MathSciNet  Google Scholar 

  15. González, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. (2008)

    Google Scholar 

  16. Guada, C., Gómez, D., Rodríguez, J.T., Yáñez, J., Montero, J.: Classifying image analysis techniques from their output. Int. J. Comput. Intell. Syst. 9, 43–68 (2016). https://doi.org/10.1080/18756891.2016.1180819

    Article  Google Scholar 

  17. Lee, D., Wang, J., Plataniotis, K.N.: Contribution of skin color cue in face detection applications. In: Celebi, M.E., Smolka, B. (eds.) Advances in Low-Level Color Image Processing. LNCVB, vol. 11, pp. 367–407. Springer, Dordrecht (2014). https://doi.org/10.1007/978-94-007-7584-8_12

    Chapter  Google Scholar 

  18. López-Molina, C.: The breakdown structure of edge detection: analysis of individual components and revisit of the overall structure. Ph.D. thesis (2012)

    Google Scholar 

  19. Macedo-Cruz, A., Pajares, G., Santos, M., Villegas-Romero, I.: Digital image sensor-based assessment of the status of oat (avena sativa l.) crops after frost damage. Sensors 11(6), 6015–6036 (2011)

    Article  Google Scholar 

  20. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images y its application to evaluating segmentation algorithms y measuring ecological statistics. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2, pp. 416–423 (2001)

    Google Scholar 

  21. McAndrew, A.: An introduction to digital image processing with matlab notes for SCM2511 image processing. Sch. Comput. Sci. Math. Victoria Univ. Technol. 264(1), 1–264 (2004)

    Google Scholar 

  22. Rojas, K., Gómez, D., Montero, J., Rodríguez, J.T., Valdivia Barrios, A., Paiva, F.: Development of child’s home environment indexes based on consistent families of aggregation operators with prioritized hierarchical information. Fuzzy Sets Syst. 241, 41–60 (2014)

    Article  MathSciNet  Google Scholar 

  23. Sandeep, K., Rajagopalan, A.: Human face detection in cluttered color images using skin color, edge information. In: ICVGIP (2002)

    Google Scholar 

  24. Shaik, K.B., Ganesan, P., Kalist, V., Sathish, B., Jenitha, J.M.M.: Comparative study of skin color detection and segmentation in HSV and YCBCR color space. Procedia Comput. Sci. 57, 41–48 (2015)

    Article  Google Scholar 

  25. Smith, A.R.: Color gamut transform pairs. In: ACM SIGGRAPH Computer Graphics, vol. 12, no. 3, pp. 12–19 (1978)

    Google Scholar 

  26. Trahanias, P.E., Venetsanopoulos, A.N.: Color edge detection using vector order statistics. IEEE Trans. Image Process. 2(2), 259–264 (1993)

    Article  Google Scholar 

  27. Turhan, H.I., Sahin, G., Erkmen, A.M.: Comparing color edge detection techniques (unpublished)

    Google Scholar 

  28. Yager, R.R.: Modeling prioritized multicriteria decision making. IEEE Trans. Syst. Man Cybernet. Part B (Cybernetics) 34(6), 2396–2404 (2004)

    Article  Google Scholar 

  29. Yager, R.R.: Prioritized aggregation operators. Int. J. Approximate Reasoning 48(1), 263–274 (2008)

    Article  MathSciNet  Google Scholar 

  30. Yang, Y.: Colour edge detection and segmentation using vector analysis. University of Toronto (1996)

    Google Scholar 

Download references

Acknowledgments

This research has been partially supported by the Government of Spain, grant PGC2018-096509-B-I00.

For conducting this research, the code created by Kermit Research Unit has been helpful [1].

Author information

Authors and Affiliations

  1. Dpto. de Producción animal, Facultad de Veterinaria, Universidad Complutense de Madrid, Av. Puerta de Hierro s/n, 28040, Madrid, Spain

    Pablo A. Flores-Vidal

  2. Dpto. de Estadística y Ciencia de los datos, Facultad de Estudios Estadísticos, Universidad Complutense de Madrid, Av. Puerta de Hierro s/n, 28040, Madrid, Spain

    Daniel Gómez & Javier Castro

  3. Dpto. de Estadística e Investigación Operativa, Facultad de Ciencias Matemáticas, Universidad Complutense de Madrid, Plaza de las Ciencias 3, 28040, Madrid, Spain

    Javier Montero

Authors
  1. Pablo A. Flores-Vidal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Gómez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Javier Castro
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Javier Montero
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pablo A. Flores-Vidal .

Editor information

Editors and Affiliations

  1. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

  2. University of Oviedo, Oviedo, Spain

    Inés Couso

  3. University of Cádiz, Cádiz, Spain

    Jesús Medina

  4. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. University of Perugia, Perugia, Italy

    Davide Petturiti

  6. Sorbonne Université, Paris, France

    Bernadette Bouchon-Meunier

  7. Iona College, New Rochelle, NY, USA

    Ronald R. Yager

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Flores-Vidal, P.A., Gómez, D., Castro, J., Montero, J. (2022). New Aggregation Strategies in Color Edge Detection with HSV Images. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1602. Springer, Cham. https://doi.org/10.1007/978-3-031-08974-9_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-08974-9_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08973-2

  • Online ISBN: 978-3-031-08974-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics