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On approximating mathematical morphology operators via deep learning techniques

Santiago Velasco-Forero 1 Bastien Ponchon Samy Blusseau 2, 3, 1 Jesus Angulo 1 Isabelle Bloch 2, 3
1 CMM - Centre de Morphologie Mathématique
2 IMAGES - Image, Modélisation, Analyse, GEométrie, Synthèse
LTCI - Laboratoire Traitement et Communication de l'Information
3 IDS - Département Images, Données, Signal
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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https://hal.telecom-paris.fr/hal-02288566
Contributor : Telecomparis Hal <>
Submitted on : Saturday, September 14, 2019 - 6:57:01 PM
Last modification on : Tuesday, September 21, 2021 - 2:16:04 PM

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  • HAL Id : hal-02288566, version 1

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LTCI | PARISTECH | INSTITUT-TELECOM | IDS | IMAGES | ENSMP_CMM | ENSMP | PSL

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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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