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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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Submitted on : Saturday, September 14, 2019 - 6:57:01 PM
Last modification on : Wednesday, November 17, 2021 - 12:27:16 PM


  • HAL Id : hal-02288566, version 1


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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