Publication:
Fuzzy integrals for edge detection

Date

2023

Director

Publisher

Springer
Acceso abierto / Sarbide irekia
Contribución a congreso / Biltzarrerako ekarpena
Versión aceptada / Onetsi den bertsioa

Project identifier

MICINN//PID2021-123673OB-C31
Métricas Alternativas
OpenAlexGoogle Scholar
No disponible en Scopus

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.

Description

Keywords

Fuzzy integrals, Choquet integral, Sugeno integral, Feature extraction, Edge detection.

Department

Estadística, Informática y Matemáticas / Estatistika, Informatika eta Matematika

Faculty/School

Degree

Doctorate program

item.page.cita

Marco-Detchart, C., Lucca, G., Dimuro, G., Asmus, T., Lopez-Molina, C., Borges, E., Rincon, J. A., Julian, V., & Bustince, H. (2023). Fuzzy integrals for edge detection. En S. Massanet, S. Montes, D. Ruiz-Aguilera, & M. González-Hidalgo (Eds.), Fuzzy Logic and Technology, and Aggregation Operators (Vol. 14069, pp. 330-341). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-39965-7_28

item.page.rights

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG.

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