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Adaptative Fuzzy Measure for Edge Detection

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Intelligent Data Engineering and Automated Learning – IDEAL 2023 (IDEAL 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14404))

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Abstract

In this work, an analysis of the influence of the fuzzy measure for Choquet integral and its generalizations is presented. The work has been done in the context of feature fusion for edge detection with gray-scale images. The particular case of adaptative fuzzy measure is considered, testing a variety of approaches. We have tested our proposal using the power measure adapting the exponent depending on the local information of each particular image. For comparison purposes and to test the performance of our proposal, we compare our approach to the results obtained with the Canny edge detector.

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Acknowledgements

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) and grant from the Research Services of Universitat Politècnica de València (PAID-PD-22).

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Authors and Affiliations

  1. Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022, Valencia, Spain

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

  2. Centro de Ciências Computacionais, Universidade Federal do Rio Grande, Av. Itália km 08, Campus Carreiros, Rio Grande, 96201-900, Brazil

    G. Lucca & G. Dimuro

  3. Valencian Graduate School and Research Network of Artificial Intelligence (VALGRAI), Universitat Politècnica de València (UPV), Camí de Vera s/n, 46022, Valencia, Spain

    J. A. Rincon & V. Julian

Authors
  1. C. Marco-Detchart
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  2. G. Lucca
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  3. G. Dimuro
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  4. J. A. Rincon
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  5. V. Julian
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Corresponding author

Correspondence to C. Marco-Detchart .

Editor information

Editors and Affiliations

  1. University of Évora, Évora, Portugal

    Paulo Quaresma

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Manchester, Manchester, UK

    Hujun Yin

  4. University of Évora, Évora, Portugal

    Teresa Gonçalves

  5. Polytechnic University of Valencia, Valencia, Spain

    Vicente Julian

  6. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

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Marco-Detchart, C., Lucca, G., Dimuro, G., Rincon, J.A., Julian, V. (2023). Adaptative Fuzzy Measure for Edge Detection. In: Quaresma, P., Camacho, D., Yin, H., Gonçalves, T., Julian, V., Tallón-Ballesteros, A.J. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham. https://doi.org/10.1007/978-3-031-48232-8_45

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  • DOI: https://doi.org/10.1007/978-3-031-48232-8_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48231-1

  • Online ISBN: 978-3-031-48232-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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