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Marco Detchart, Cedric

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https://academica-e.unavarra.es/handle/2454/49498

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Marco Detchart

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Cedric

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AutomĆ”tica y ComputaciĆ³n

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0000-0002-4310-9060

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810938

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2021 - 20232
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Subject: Choquet integral Ɨ

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Now showing 1 - 2 of 2
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    PublicationOpen Access
    Fuzzy integrals for edge detection
    (Springer, 2023) Marco Detchart, Cedric; Lucca, Giancarlo; Pereira Dimuro, GraƧaliz; Da Cruz Asmus, Tiago; LĆ³pez Molina, Carlos; Borges, Eduardo N.; Rincon, J. A.; Julian, Vicente; Bustince Sola, Humberto; EstadĆ­stica, InformĆ”tica y MatemĆ”ticas; Estatistika, Informatika eta Matematika
    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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    PublicationOpen Access
    Neuro-inspired edge feature fusion using Choquet integrals
    (Elsevier, 2021) Marco Detchart, Cedric; Lucca, Giancarlo; LĆ³pez Molina, Carlos; Miguel Turullols, Laura de; Pereira Dimuro, GraƧaliz; Bustince Sola, Humberto; EstadĆ­stica, InformĆ”tica y MatemĆ”ticas; Estatistika, Informatika eta Matematika; Universidad PĆŗblica de Navarra / Nafarroako Unibertsitate Publikoa
    It is known that the human visual system performs a hierarchical information process in which early vision cues (or primitives) are fused in the visual cortex to compose complex shapes and descriptors. While different aspects of the process have been extensively studied, such as lens adaptation or feature detection, some other aspects, such as feature fusion, have been mostly left aside. In this work, we elaborate on the fusion of early vision primitives using generalizations of the Choquet integral, and novel aggregation operators that have been extensively studied in recent years. We propose to use generalizations of the Choquet integral to sensibly fuse elementary edge cues, in an attempt to model the behaviour of neurons in the early visual cortex. Our proposal leads to a fully-framed edge detection algorithm whose performance is put to the test in state-of-the-art edge detection datasets.