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Communication Dans Un Congrès Année : 2019

On approximating mathematical morphology operators via deep learning techniques

Résumé

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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Dates et versions

hal-02288566 , version 1 (14-09-2019)

Identifiants

  • HAL Id : hal-02288566 , version 1

Citer

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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