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morpholayers

Morphological Layers for Keras/Tensorflow2 The goal of morpholayers is to make the interactions between mathematical morphology and deep learning accessible for everyone.

!git clone https://github.com/Jacobiano/morpholayers.git

If you find this code useful in your research, please consider citing:

@inproceedings{VelascoBMVC2022,
Author = {Velasco-Forero, S. and Rhim, A. and Angulo, J.},
Title = {Fixed Point Layers for Geodesic Morphological Operations},
Booktitle  = {British Machine Vision Conference (BMVC)},
Year = {2022}
}


@article{VelascoSIAM2022,
author = {Velasco-Forero, Santiago and Pag\`{e}s, R. and Angulo, Jesus},
title = {Learnable Empirical Mode Decomposition based on Mathematical Morphology},
journal = {SIAM Journal on Imaging Sciences},
volume = {15},
number = {1},
pages = {23-44},
year = {2022},
}

Several examples of this library are available at: Examples

ECSIA mini-cours (Mathematical morphology meets Deep Learning)

Talks:

  • Introduction
  • Deep Learning in 15 minutes
  • Mathematical morphology: Learning simple operators
  • Depthwise Morphological Layers
  • Morphological Scale-Spaces

Practical Sessions:

-Tutorial 0: Deep Learning in 15 minutes

Tutorial 1: Simple morphological operators using morpholayers

Tutorial 2: Learning morphological operators

Tutorial 3: Learning morphological layers in Fashion Mnist

Tutorial 4: Improving Max-Pooling layers using Dilations

Tutorial 5: Learning Additive Shift Equivariant Operators

Tutorial 6: Learning Scale-Equivariant Operators

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