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Fuzzy Integrals for Edge Detection

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Fuzzy Logic and Technology, and Aggregation Operators (EUSFLAT 2023, AGOP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14069))

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Abstract

In this work, we compare different families of fuzzy integrals in the context of feature aggregation for edge detection. We analyze the behaviour of the Sugeno and Choquet integral and some of its generalizations. In addition, we study the influence of the fuzzy measure over the extracted image features. For testing purposes, we follow the Bezdek Breakdown Structure for edge detection and compare the different fuzzy integrals with some classical feature aggregation methods in the literature. The results of these experiments are analyzed and discussed in detail, providing insights into the strengths and weaknesses of each approach. The overall conclusion is that the configuration of the fuzzy measure does have a paramount effect on the results by the Sugeno integral, but also that satisfactory results can be obtained by sensibly tuning such parameter. The obtained results provide valuable guidance in choosing the appropriate family of fuzzy integrals and settings for specific applications. Overall, the proposed method shows promising results for edge detection and could be applied to other image-processing tasks.

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Acknowlegements

This work was partially supported with grant PID2021-123673OB-C31 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”, Consellería d’Innovació, Universitats, Ciencia i Societat Digital from Comunitat Valenciana (APOSTD/2021/227) through the European Social Fund (Investing In Your Future), grant from the Research Services of Universitat Politècnica de València (PAID-PD-22), FAPERGS/Brazil (Proc. 19/2551-0001279-9, 19/2551-0001660) and CNPq/Brazil (301618/2019-4, 305805/2021-5, Edital 07/2022), Programa de Apoio á Fixação de Jovens Doutores no Brasil (23/2551-0000126-8).

Author information

Authors and Affiliations

  1. Valencian Research Inst. Artificial Intelligence, Univ. Politècnica de València, Valencia, Spain

    C. Marco-Detchart, J. A. Rincon & V. Julian

  2. Valencian Graduate School and Research Network of Artificial Intelligence, Universitat Politècnica de València, Valencia, Spain

    J. A. Rincon & V. Julian

  3. Centro de Ciências Computacionais, Univ. Federal do Rio Grande, Porto Alegre, Brazil

    G. Lucca, G. Dimuro & E. Borges

  4. Dept. Estadistica, Informa. y Matem., Univ. Publica de Navarra, Pamplona, Spain

    C. Lopez-Molina & H. Bustince

  5. Navarrabiomed, Hospital Univ. de Navarra (HUN), IdiSNA, Pamplona, Spain

    C. Lopez-Molina & H. Bustince

  6. Instituto de Matemática, Estadística e Física, Univ. Federal do Rio Grande, Porto Alegre, Brazil

    T. Asmus

Authors
  1. C. Marco-Detchart
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  2. G. Lucca
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  3. G. Dimuro
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  5. C. Lopez-Molina
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  7. J. A. Rincon
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  8. V. Julian
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  9. H. Bustince
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Corresponding author

Correspondence to C. Marco-Detchart .

Editor information

Editors and Affiliations

  1. University of the Balearic Islands, Palma, Spain

    Sebastia Massanet

  2. University of Oviedo, Gijón, Spain

    Susana Montes

  3. University of the Balearic Islands, Palma, Spain

    Daniel Ruiz-Aguilera

  4. University of the Balearic Islands, Palma, Spain

    Manuel González-Hidalgo

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Marco-Detchart, C. et al. (2023). Fuzzy Integrals for Edge Detection. In: Massanet, S., Montes, S., Ruiz-Aguilera, D., González-Hidalgo, M. (eds) Fuzzy Logic and Technology, and Aggregation Operators. EUSFLAT AGOP 2023 2023. Lecture Notes in Computer Science, vol 14069. Springer, Cham. https://doi.org/10.1007/978-3-031-39965-7_28

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  • DOI: https://doi.org/10.1007/978-3-031-39965-7_28

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  • Online ISBN: 978-3-031-39965-7

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