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Communication dans un congrès

On approximating mathematical morphology operators via deep learning techniques

Abstract : Mathematical Morphology (MM) is a well-established discipline whose aim is mainly to provide tools to characterise complex object via their shape/size features. This study addresses the problem of robust approximation of mathematical morphology (MM) operators by deep learning methods. We present two cases, (a) Asymmetric autoencoders for part-based approximations of classical MM in the sense of [1] and, (b) image-to-image translation networks [2] to produce robust MM operators in presence of noise.
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Communication dans un congrès
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https://hal.telecom-paris.fr/hal-02288566
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Soumis le : samedi 14 septembre 2019 - 18:57:01
Dernière modification le : samedi 22 octobre 2022 - 05:10:11

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  • HAL Id : hal-02288566, version 1

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Santiago Velasco-Forero, Bastien Ponchon, Samy Blusseau, Jesus Angulo, Isabelle Bloch. On approximating mathematical morphology operators via deep learning techniques. 15th International Congress for Stereology and Image Analysis, 2019, Aarhus, Denmark. pp.51. ⟨hal-02288566⟩

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