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New Aggregation Strategies in Color Edge Detection with HSV Images

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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))

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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.

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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.

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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
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  2. Daniel Gómez
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  3. Javier Castro
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  4. Javier Montero
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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

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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

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

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  • Online ISBN: 978-3-031-08974-9

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