Binary Multi Channel Morphological Neural Network - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Preprints, Working Papers, ... Year : 2022

Binary Multi Channel Morphological Neural Network


Neural networks and particularly Deep learning have been comparatively little studied from the theoretical point of view. Conversely, Mathematical Morphology is a discipline with solid theoretical foundations. We combine these domains to propose a new type of neural architecture that is theoretically more explainable. We introduce a Binary Morphological Neural Network (BiMoNN) built upon the convolutional neural network. We design it for learning morphological networks with binary inputs and outputs. We demonstrate an equivalence between BiMoNNs and morphological operators that we can use to binarize entire networks. These can learn classical morphological operators and show promising results on a medical imaging application.
Fichier principal
Vignette du fichier
Bimonn_DGMM_2022_Preprint_for_Arxiv.pdf (774.71 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03644498 , version 1 (19-04-2022)


  • HAL Id : hal-03644498 , version 1


Théodore Aouad, Hugues Talbot. Binary Multi Channel Morphological Neural Network. 2022. ⟨hal-03644498⟩
93 View
38 Download


Gmail Facebook X LinkedIn More